PRIN 2022 CALL. DECREE NO. 104 OF 2 FEBRUARY 2022

TITOLO DEL PROGETTO: Metrology for spintronics: A machine learning approach for the reliable determination of the Dzyaloshinskii-Moriya interaction (MetroSpin)

CUP CODE:  D53D23002360006

Budget:  € 77.095

U.R. Coordinator: Vito Puliafito 
Other Research Units or any Sub-Units: 
National Metrology Research Institute of Turin (PI’s U.R.) and University of Perugia

Brief project description
Despite the strong scientific and industrial growth of spintronics, as a low-energy alternative to CMOS technology and compatible with it, the commercial diffusion of spintronic technologies is limited by the absence of reliable metrological standards for measuring the key parameters of devices.

The project proposed a study focused on the Dzyaloshinskii-Moriya interaction (DMI), a fundamental parameter for the stability of chiral spin structures used in low-power memories and logic devices. Following previous European studies that defined good experimental practices, a statistical approach capable of explaining the remaining discrepancies in measurements was still lacking.

The objective was therefore to analyse the reproducibility and repeatability of measurements, correlating data dispersion with defects and inhomogeneities in the samples, and to develop machine learning methods to extract DMI from simulated and experimental magnetic patterns. Artificial intelligence is being assessed as a possible metrological tool to improve measurement reliability, with potential impacts not only in spintronics but across all nanotechnology sectors facing similar issues.

Purpose
The project aimed to improve the reliability of spintronics measurements through a statistical approach based on machine learning, with particular attention to the measurement of the Dzyaloshinskii-Moriya interaction (DMI), a key parameter for innovative spintronic devices. The activities focused on reducing uncertainty and discrepancies among different measurement techniques, analyzing the physical origin of data dispersion, and developing automated analysis procedures based on artificial intelligence. The project also assessed the possible extension of these approaches to other areas of spintronics and nanotechnologies.

Expected results
The expected results of the project included a better understanding of the physical and statistical causes underlying the dispersion of DMI measurements, the identification of strategies to reduce experimental uncertainty and improve the reproducibility of measurement techniques, as well as the development of automated analysis tools based on machine learning. The project also aimed to demonstrate the potential of artificial intelligence as a metrological support for spintronics and, more generally, for applications in nanotechnologies characterized by similar measurement reliability issues.

Achieved results 
The project successfully achieved three main objectives.

• Accurate determination of the interfacial Dzyaloshinskii-Moriya interaction (iDMI). Guidelines were developed for the two main iDMI measurement techniques: Brillouin light spectroscopy (BLS) and the asymmetric expansion of magnetic bubbles measured via the magneto-optical Kerr effect (MOKE). The guidelines define the key technical aspects required to obtain accurate measurements, include reproducibility studies, analyze the dependence on intrinsic and extrinsic parameters, and compare different iDMI evaluation models. This provides a reliable tool for assessing the quality, performance, and energy consumption of spintronic devices.

• Resolution of discrepancies between measurement techniques. Following the developed guidelines, it was possible to significantly reduce the measurement uncertainty for both methods. The large discrepancies in iDMI values reported in the literature were successfully attributed to the presence of chiral damping, which affects the bubble expansion technique differently from BLS. Thanks to a model developed within the project, it was possible to obtain consistent iDMI values within experimental uncertainty limits for both techniques. This result significantly improves the understanding of the physics of domain wall motion in the presence of iDMI.

• Feasibility of statistical approaches and integration of artificial intelligence. The potential of using machine learning for rapid, high-throughput iDMI analysis, including statistical aspects, was demonstrated through a study on synthetic data simulating the bubble expansion technique. The model proved robust even in the presence of simulated defects and blurred images. Once suitable databases become available, this work may lead to the development of a fast and accessible AI-based tool for the classification of spintronic devices.
   

PRIN 2022 CALL. DECREE NO. 104 OF 2 FEBRUARY 2022

PROJECT TITLE: Innovative mathematical models for soft matter and hierarchical materials

CODICE CUP:  D53D23006020006

Budget: € 95,970

P.I.: Giuseppe Puglisi
Heads of other research units:
Luciano TERESI University of Rome TRE
Luca DESERI University of Trento
Arsenio CUTOLO University of Naples Federico II

Brief project description
The project "Innovative mathematical models for soft matter and hierarchical materials" was aimed at developing multiscale mathematical models for the study of soft and complex materials, from biomaterials to nature-inspired hierarchical materials. The approach is interdisciplinary and integrates statistical mechanics, nonlinear continuum mechanics, multiphysics modeling, numerical simulation, experimental analyses, and physically grounded machine learning methods. The activities were organized along four research lines coordinated by the respective units: multiscale models for biological and polymeric materials (UR Poliba, PI: Giuseppe Puglisi), morphogenesis mechanics (UR Roma Tre), instabilities and wrinkling of soft materials on substrates (UR Trento), and cellular and multiphysics mechanics (UR Napoli). The project was funded by the Ministry of University and Research (MUR). 

Purpose
The fundamental objective was to establish a direct connection between the macroscopic behavior of materials and their micro- and nanometric-scale properties, through combined theoretical, computational, and experimental analyses. The Research Unit of the Polytechnic University of Bari aims in particular to develop predictive models for bioinspired materials — with special attention to silk and the artificial fibers inspired by it — integrating statistical mechanics, phase-transition modeling, and physically grounded machine learning. The Poliba activities are part of Line A of the project, dedicated to multiscale approaches for biological and polymeric materials, with applications in the fields of advanced materials, engineering, and biomechanics. 

Expected results
The activities of the Research Unit of the Polytechnic University of Bari aimed to develop multiscale analytical models capable of linking the molecular and microstructural structure of biomaterials to their macroscopic response. The expected outcome was the development of new physically based analytical tools for the constitutive description of the behavior of complex materials — including fibrous systems, protein-based materials, and soft materials subject to varying environmental conditions — as well as advances in the understanding of the mechanisms governing phase transitions, fracture, adhesion, and thermal fluctuations. In terms of dissemination, the production of publications in high-impact international journals and the creation of a stable scientific network among the groups involved were envisaged.

Achieved results (this section will not be completed for the PRIN 2022 projects subject to progression, as they are still ongoing)
The activities of the Research Unit of the Polytechnic University of Bari followed a coherent scientific path, linking statistical mechanics, nonlinear continuum mechanics, and multiscale modeling within a theoretical framework applied to the study of soft and hierarchical materials. The thermodynamic limits and the role of thermal fluctuations in multistable systems were clarified, with applications to fracture, adhesion, and phase transitions. A central line of research concerned the modeling of spider silk and bioinspired materials: supercontraction was interpreted as a humidity-driven solid-solid phase transition, and the coupled contraction-torsion response was traced back to a dual Poynting effect. Physically grounded machine learning approaches supported the development of interpretable analytical models for these complex systems.

On the experimental side, thanks to the project funding, an existing biaxial testing machine was upgraded with a temperature- and humidity-controlled climatic chamber, new sensors, and a stereo-DIC optical system, making it possible to acquire fundamental knowledge on the coupled effects of temperature and humidity on the mechanical behavior of the materials studied.

The results were disseminated through publications in high-impact international journals and contributions to national and international conferences. The project also fostered the building of a scientific network with other national research groups operating in related fields, consolidated through the organization of joint meetings among PRIN projects.

The project was funded by the Ministry of University and Research (MUR).
 

PRIN 2022 CALL. DECREE NO. 104 OF 2 FEBRUARY 2022

PROJECT TITLE:  CAMEO - Conversational Agents: Mastering, Evaluating, Optimizing

CODICE CUP:  D53D23008920006

Budget:  € 77,794.00

P.I. or U.R. Manager: Tommaso Di Noia

Other Research Units or any Sub-Units: 
UNIPD - University of Padua 
CNR - National Research Council 
POLIBA - Polytechnic University of Bari 
ROMA1 - Sapienza University of Rome

Brief project description
Conversational agents are now widely used in chatbots, smartphones, and smart home devices such as Google Home, Alexa, and Siri. However, they still struggle to support natural, engaging, and context-aware real-time interactions. 
The CAMEO project addressed these limitations by leveraging user context as an internal knowledge source, capturing information from past interactions and enriching it with domain knowledge. This enabled more effective dialogue management, query answering, personalized search, and recommendation. 

Purpose
A key contribution of CAMEO is the integration of search and recommendation within a unified framework, along with improved evaluation methodologies to better understand and optimize conversational agent behavior. 

Expected results
Definition of a semantic search engine for querying the thesaurus that will make it possible to retrieve headwords and analyse their semantic evolution, highlighting statistics, such as the frequency of a term over time, variations in the meaning of a term, more co-occurring terms. Querying the thesaurus will make it possible to trace and highlight the cultural reflection that took place between the two world wars in France. The thesaurus will be available in open access. 
The dissemination activity includes an international conference at which the results of the research will be presented and a subsequent publication of the proceedings in an open access journal. The project also includes extensive public engagement activities, such as lectures in higher education and summer schools.

Achieved results (this section will not be completed for the PRIN 2022 projects subject to progression, as they are still ongoing)
The project successfully established a novel paradigm of joint conversational search and recommendation, integrating catalogue-based recommendation with open-world search and advancing the theoretical foundations of conversational systems. A key outcome is the development and release of CosRec, the first human-annotated dataset specifically designed for joint conversational search and recommendation, providing a unique and reproducible benchmark for future research. 
The project also laid the groundwork for the integration of conversational systems with emerging Large Language Models (LLMs), highlighting both their potential as natural language interfaces and current limitations in personalization and reliability. By grounding conversational agents in structured knowledge sources, the project offers a robust foundation for future developments. Overall, the results significantly advance the state of the art and provide essential resources and directions for the continued evolution of conversational search and recommendation systems. 
 

PRIN 2022 CALL. DECREE NO. 104 OF 2 FEBRUARY 2022

PROJECT TITLE: TRex-SE: Trustworthy Recommenders x Software Engineers 

CODICE CUP:  D53D23008730006

Budget:  € 87.060,00

P.I. or U.R. Manager: Fedelucio Narducci

Other Research Units or any Sub-Units: 
University of SANNIO of BENEVENTO (Unisannio) 
University of L'AQUILA (Univaq) 

Brief project description
Recommender Systems for Software Engineers (RSSEs) support development tasks by suggesting APIs, code snippets, or experts for change management. The availability of data from platforms such as GitHub and Stack Overflow, together with advances in machine learning, has significantly improved their capabilities. However, this abundance of training data also introduces risks. Recommended code may be vulnerable or malicious, especially when popularity-based metrics are manipulated through fake projects or misleading discussions. Moreover, developers need transparency regarding the provenance and trustworthiness of recommendations, as security, privacy, and legal concerns are increasingly relevant—also in light of emerging AI regulations. 
In addition, RSSEs rarely incorporate user feedback effectively. Developers should be able to accept, reject, or modify recommendations, enabling human-in-the-loop scenarios that improve system accuracy and reliability. 

Purpose
The TRex-SE (Trustworthy Recommenders x Software Engineers) project aims to address these challenges by developing methods to enhance the trustworthiness of RSSEs. 

Expected results
Identification of vulnerabilities and bias in RSSE recommendations: 
After assessing the presence of vulnerable (or in any case, risky) recommendations provided by RSSEs, as well as of recommendations affected by bias, this objective aims at developing vulnerability and bias-aware mining tools for RSSEs. 
Design and Evaluation of Trustworthy RSSEs: After analyzing the techniques and methodologies for designing fair, unbiased, and secure RSs, this objective aims at designing and evaluating recommendation approaches for trustworthy RSSEs. Human-in-the-loop in RSSEs: After analyzing existing RSs that provide human-in-the-loop capabilities, this objective aims to develop techniques and tools for managing user feedback in the context of RSSEs.

Achieved results (this section will not be completed for the PRIN 2022 projects subject to progression, as they are still ongoing) 
Identification of vulnerabilities and bias in RSSEs 

• Developing LLM-based mining and detection tools (e.g., multi-agent malware detection, semantic noise filtering) to identify vulnerable, poisoned, or inconsistent data.
• Analyzing bias and reliability issues in LLM outputs and benchmarks (e.g., prompt sensitivity, multilingual bias, data leakage).
• Building taxonomies and empirical studies to understand risks in AI-assisted development and RSSE inputs. 
   

Design and evaluation of trustworthy RSSEs 

• Designing trustworthy recommendation approaches addressing privacy (differential privacy), fairness (bias mitigation), and reproducibility (DataRec).
• Developing LLM-based tools and prototypes (e.g., SATD classification, CI/CD recommendation, test generation).
• Performing extensive empirical validation, highlighting both the effectiveness and limitations of LLM-based RSSEs. 
   

Human-in-the-loop in RSSEs 

• Creating feedback-aware tools that leverage developer input (e.g., SATD detection, improved knowledge retrieval).
• Designing mechanisms to integrate and validate user feedback in RSSE workflows.
• Enhancing transparency and trust, e.g., through AI-generated code detection (GPTSniffer) and improved interaction with LLMs.
   

Critical and Emerging Technologies (STEP) – DM 25/10/2024

National Program for Research, Innovation and Competitiveness 2021-27 (Fund for Sustainable Growth)

PROJECT TITLE: 

CUP CODE: B99J25000470005

Politecnico di Bari Budget: € 679.499,52

P.I. or Responsible U.R. : Prof. Mario Carpentieri

Partner: 

CARPENTERIA SALENTINA S.R.L. 

TECNOLOGIE STRADALI S.R.L. UNIPERSONALE 

PROJECT SYNTHESIS  

The Wi-Road project aims to develop an innovative infrastructural system for dynamic charging of electric vehicles through DWPT technology (Dynamic Wireless Power Transfer), completely roadside. The goal is to design, realize, and validate a first integrated prototype in a controlled environment capable of transferring power inductively to moving electric vehicles, overcoming the current limitations of static charging.

The initiative consists of a coordinated series of industrial research and experimental development activities. Partners collaborate on the development of a scalable, modular, and interoperable system designed to withstand harsh operating conditions (heavy traffic, thermal excursions, atmospheric agents), maximizing energy efficiency while ensuring safety, durability, and electromagnetic compatibility.

The project is based on three main axes:

• the physical integration of DWPT components into the roadway, focusing on mechanical resistance and maintenance accessibility;

• the design and realization of the transmitter module, which includes converters, coils, resonant compensations, and intelligent control systems;

• the electromagnetic and functional modeling and optimization, culminating in the development of predictive activation strategies capable of dynamically adapting the system's behavior to real operational conditions.

The final objective is to obtain a pre-industrializable technological demonstrator, which represents a concrete foundation for future applications on an urban and extra-urban scale, promoting the spread of electric mobility in terms of environmental sustainability and operational autonomy.

OBJECTIVES

The final objective of the Wi-Road project is the development and validation in a controlled environment of an intelligent road infrastructure for dynamic charging of electric vehicles using DWPT technology (Dynamic Wireless Power Transfer), fully located on the infrastructure side. The system will be capable of transferring energy wirelessly from the roadway to a moving electric vehicle, enabling continuous, sustainable, and zero local emission electric mobility, overcoming autonomy limits related to battery capacity and the need for stops for charging.

The project aims to create a functioning prototype on a test track, consisting of two main modules:

• a roadway module designed to physically and functionally integrate the transmitting subsystems within the pavement, ensuring compatibility with vehicular traffic, durability, and simplified maintenance.

• a transmitting module, containing power electronics, coils, and compensation circuits, equipped with dynamic activation logic and communication interfaces, which ensures optimal energy transfer to moving vehicles.

In parallel, the project includes activities in electromagnetic modeling, development of predictive control algorithms, and experimental validation of the system, with the involvement of academic and industrial partners with complementary expertise.

EXPECTED RESULTS 

The Wi-Road project aims to create a functioning prototype of a roadway system for dynamic wireless charging of electric vehicles. The main results will concern the design, integration, and experimentation, in a controlled environment, of a section of road infrastructure that hosts a fully operational DWPT (Dynamic Wireless Power Transfer) system.

Specifically, the project includes:

• the detailed design of the road surface with materials and configurations compatible with the presence of transmitting devices, without compromising the structural characteristics of the pavement;

• the development of the transmitting module, including electronic components and control logic for inductive energy transfer;

• the modeling and simulation of the electromagnetic behavior of the system under various traffic conditions and road configurations;

• the physical realization of a prototype segment equipped with the DWPT system and tested in a laboratory setting on a test track;

• and finally, the development of algorithms for the dynamic and predictive activation of transmitting segments, in order to optimize energy consumption and reduce potential interference.

The combination of these results will provide a concrete basis for the future implementation of the system on a larger scale, with applications in urban, logistics, and highway contexts.

The DWPT technology represents an emerging frontier in the field of electric mobility, but its actual application is still limited to a few pilot experiments. In this sense, the Wi-Road project stands out for its integrated infrastructural approach, combining expertise in road design, power electronics, and control systems.

Compared to the state of the art, the originality of the project does not lie in a single innovative component, but in the ability to coherently integrate multiple technologies, developing a complete, installable, testable, and potentially replicable system.

In addition to addressing the technical aspects of power transfer, the project aims to solve concrete problems related to pavement durability, maintenance, electromagnetic safety, and compatibility with existing vehicles. It is therefore not an incremental improvement of existing solutions, but a step forward towards a new type of infrastructure, designed to support the growth of electric mobility more effectively and sustainably.

At the European level, similar experiences have so far been limited to conceptual demonstrators. The Wi-Road project, on the other hand, aims to produce a tangible result, tested and ready to be integrated into future real infrastructure projects, also in collaboration with public entities or highway concessionaires.

Thanks to a solid design framework and a strong grounding in the technical expertise of the involved partners, Wi-Road can represent a true technological leap for the road infrastructure sector and for the entire electric charging supply chain.

In addition to improving the performance and flexibility of charging systems, the project will contribute to stimulating new solutions in the field of paving materials, embedded sensors, intelligent control systems, and interoperability models between vehicle and infrastructure. Looking forward, the results could also impact the urban logistics sector, electric public transport, and decarbonization policies in various regions..

PRIN 2022 CALL. DECREE NO. 104 OF 2 FEBRUARY 2022

TITOLO DEL PROGETTO: Organizational success factors of sustainability-oriented firms: an analysis of Italian firms in different phases of their life cycle 

CUP CODE D53D23006640006

Budget: € 140.655

P.I.: Prof. Rosa Maria Dangelico (Polytechnic University of Bari)

Other Research Units: University of Palermo; University of Messina

The project involved three research units (Polytechnic University of Bari, University of Palermo, and University of Messina) and analysed organizational success factors for sustainability-oriented firms in different phases of their life cycle. Specifically, the aim of the project was to analyze the effects of different intra-organizational factors and inter-organizational factors on firm performance. Different dimensions of performance have been considered, namely economic, environmental, and social. Based on the literature, a theoretical framework linking organizational factors and firm performance was developed. The project studied 406 Italian firms, comprising a sample of 203 firms that obtained B Corp certification and a control sample of 203 firms. These samples include companies at different stages of their life cycle (startups and mature firms). Data have been collected since 2014 from several commercial databases (i.e., Orbis, Orbis IP, and Zephyr) and other sources (i.e., LinkedIn, company websites, and corporate sustainability reports). Text-mining techniques were used to retrieve data on sustainable process patterns from 548 corporate sustainability reports; these were validated through qualitative analysis to assess emerging research tools such as Large Language Models (LLMs). Panel data regression models were applied to test the hypotheses developed. 

Expected results refer to the sustainable business process patterns and capabilities implemented by sustainability-oriented firms and to the effects of intra- and inter-organizational factors on different dimensions of firm performance. 

Achieved results are: the creation of a unique and original database with data on intra-organizational factors (managerial team characteristics and sustainable business process patterns and capabilities) and inter-organizational factors (in terms of strategic collaborations), as well as different dimensions of firm performance (ROI, patents, environmental certifications, social certifications); the implementation rate of different sustainable capabilities and patterns by sustainability-oriented firms and a comparison of the implementation rate of capabilities implemented by startup and mature firms; the effects of the managerial team characteristics and strategic collaborations on firm performance and the moderating role of sustainability orientation; the effects of the managerial team characteristics and strategic collaborations and the moderating role of firm age; the effects of the managerial team characteristics, strategic collaborations, and sustainable capabilities on firm performance.

Based on the achieved results, managerial and policy guidelines have been developed.

The results obtained have been disseminated at conferences and at a workshop organized at the Politecnico di Bari.

PRIN 2022 CALL. DECREE NO. 104 OF 2 FEBRUARY 2022

PROJECT TITLE: MId infRAred laBel free Interferometric detectorLess Imaging in photonic circuitS - MIRABILIS 

CODICE CUP: D53D23002780006

Budget: € 74.871

P.I. or Head of the Research Unit: Massimo Brambilla (P.I.)

Other Research Units or any Sub-Units: PoliTo, UniBa

Brief project description
The project MIRABILIS is meant to combine three key features in optical imaging within biologically relevant spectral windows of the radiation (mid-infrared): a) the implementation of Single Pixel Imaging (SPI) of a target, using an illumination provided by a speckle field pattern, generated either in free space or in a photonic platform; b) the use of Optical Feedback Interferometry (OFI) in a diode laser or in a Quantum Cascade Laser (QCL) to gather target properties provided in the sPI scheme described above, without making use of an external detector; c) characterize and classify target features using machine-learning algorithms. 

Purpose
The project MIRABILIS is formulated to achieve phantom tissue imaging in the Mid-Infrared, based on Optical Feedback Interferometry (OFI) in a Quantum Cascade Laser (QCL) and Single Pixel Imaging (SPI), using an illumination provided by a speckle field pattern. The validation was to be provide both in free space and in a photonic platform (Optical Phase Array, OPA) 

Expected results
The three main expected results and innovative elements of the project are: a) the use of a Photonic Integrated Circuit (PIC) such as an OPA to provide controlled, multiple, illumination patterns of a target, encompassing a programmable stage to comply with the following item; b) the collection of reflected light patterns from the target and their reduction to a one-dimensional signal to be treated with Single-Pixel Imaging (SPI) techniques, with the additional contribution of Machine Learning (ML) based algorithms; c) the discrimination and processing of such SPI signal by the master laser itself in a
Self-Mixing-Interferometry scheme, which leads to reconstruct target information from the voltage measurements at the laser contacts

Achieved results
MIRABILIS assessed the possibility of exploiting Compressed Sensing (CS) / SPI imaging methods either using an external detector or, most relevant, in a configuration of OFI scheme. where the laser providing target illumination also detects the feedback by measuring the voltage variations at the laser contacts. The image discrimination and classification was pursued by means of state-of-the-art ML techniques. In parallel image reconstruction was obtained 

based on speckle illumination and single-pixel signal collection. All the above goals were complemented by innovative modelling and simulations in prediction, support and steering thereof.
The second achievement of the project is the design and validation of OPA in the mid-infrared, opening the road to silicon-platform-based imaging with photonic devices. In the MIR spectral region, the design of a reliable PDKs (Process Design Kit) required the novel design and simulations of all the necessary elements via commercial and self-developed tools. 

In the near-infrared spectral range, a prototype of a short range OPA operating at 1500 nm was obtained via a collaboration with ST-Microelectronics and allowed for the first proof-of-principle of
OFI imaging with a double passage through a complex PIC.  

 

PRIN 2022 CALL. DECREE NO. 104 OF 2 FEBRUARY 2022

TITOLO DEL PROGETTO: HERA (Holistic Energy Recovery Agent) 

CUP CODE: D53D23003910006

Budget: € 68.500

U.R. Coordinator: Prof. Umberto Berardi

Other Research Units or any Sub-Units:

• University of Naples Federico II (P.I.: Prof. Annamaria Buonomano)

• Sapienza University of Rome (U.R. Coordinator – Prof. Davide Astiaso Garcia)

Brief project description

The HERA project (Holistic Energy Recovery Agent) is situated within the context of the urban energy transition, addressing the growing need to reduce emissions, improve energy efficiency and increase the resilience of urban systems. In particular, the project focuses on developing a holistic approach to the design and management of sustainable energy districts, understood as clusters of interconnected buildings capable of interacting intelligently with energy networks. These systems integrate renewable energy sources, storage systems and advanced energy management strategies, also incorporating the role of electric mobility and user behaviour. The proposed approach is based on a multi-scale dynamic simulation platform that makes it possible to model buildings, energy infrastructures and energy flows, assessing their performance in energy, environmental and economic terms.

Purpose

The aims of the project are geared towards supporting the design and optimisation of resilient, flexible and low-emission energy districts, promoting the integration of urban planning and energy. In this regard, HERA aims to address existing gaps in urban modelling tools by developing solutions capable of representing buildings, energy systems and networks in an integrated manner, and of supporting decision-making processes through advanced simulations and optimisation algorithms. The project also aims to encourage the adoption of innovative strategies such as demand response, energy sharing and sector coupling, helping to improve the energy self-sufficiency of districts and reduce dependence on fossil fuels. 

Expected results

The expected results of the project include, first and foremost, the development of an innovative simulation and optimisation platform capable of analysing the energy behaviour of complex urban districts and identifying the most efficient configurations in terms of technologies, systems and management strategies. This will be complemented by the definition of advanced models for representing buildings and energy systems, including simplified models (Reduced Order Models) to reduce computational times and facilitate large-scale application. The project also envisages the development and analysis of real and representative case studies, which will be useful for validating the methodologies developed and for generating transferable datasets and knowledge. A further outcome will be the development of guidelines and design criteria for the implementation and management of sustainable energy districts, as well as the development of advanced energy management strategies based on optimisation, artificial intelligence and predictive models. 

Achieved results 

At the conclusion of the project activities, the HERA project achieved significant results in both scientific and applied terms, making a concrete contribution to the advancement of knowledge and tools for the energy transition of urban systems.

First, an innovative dynamic simulation and optimization platform was developed, capable of integrally modelling the energy behaviour of building clusters and complex urban districts, based on a long-term multi-node optimization model (H2RES). This platform makes it possible to analyse the interactions between energy demand, renewable energy production, storage systems and network infrastructure, thereby identifying optimal configurations in terms of energy efficiency, emissions reduction and system flexibility. 

In parallel, the project led to the development of advanced models for representing buildings and energy systems, including simplified models (Reduced Order Models) capable of ensuring a good balance between accuracy and computational time. These tools made it possible to efficiently simulate entire urban districts, enabling the analysis of complex scenarios and the exploration of different retrofit and energy management strategies.

A further result concerns the definition and implementation of advanced energy management strategies based on demand response, energy sharing and multi-vector integration (electricity, thermal energy, mobility). In this context, optimization algorithms and approaches based on artificial intelligence and machine learning were developed, capable of improving the management of energy flows and increasing the self-sufficiency and resilience of districts, including in the presence of intermittent renewable energy sources.

PRIN 2022 CALL. DECREE NO. 104 OF 2 FEBRUARY 2022

TITOLO DEL PROGETTO: 20227Y5NZ2 - Reshaping the Role of Measurement in the 4.0 Era: towards a Cyber-Physical Measurement system for Advanced Monitoring Applications

CUP CODE: D53D23001050006

Budget: € 84.960,00

Poliba R.U. CoordinatorDr. Nicola Giaquinto

National P.I.: Leopoldo Angrisani - University of ROME "La Sapienza"

Brief project description

The project concerns the evolution of measurement and monitoring systems in the context of cyber-physical systems and Industry 4.0 technologies. The objective is to overcome the traditional role of measurement systems as simple data acquisition tools, transforming them into active components capable of integrating measurement, modelling, simulation and advanced processing.

The project develops the Cyber-Physical Measurement Systems (CPMS) paradigm. The case study concerns distributed measurement systems based on time-domain reflectometry (TDR), applied to the detection of water and variations in the dielectric properties of the monitored medium. The research unit of the Polytechnic University of Bari contributes primarily to the development of the digital twin of the measurement system, through models capable of reproducing the behaviour of distributed TDR systems under real and non-ideal conditions, and of supporting TDR inversion, simulation and uncertainty evaluation.

Purpose

The main objectives of the research unit of the Polytechnic University of Bari concern

CUP CODE: D53D23001050006

Budget: € 84.960,00

Poliba R.U. CoordinatorDr. Nicola Giaquinto

National P.I.: Leopoldo Angrisani - University of ROME "La Sapienza"

Brief project description

The project concerns the evolution of measurement and monitoring systems in the context of cyber-physical systems and Industry 4.0 technologies. The objective is to overcome the traditional role of measurement systems as simple data acquisition tools, transforming them into active components capable of integrating measurement, modelling, simulation and advanced processing.

The project develops the Cyber-Physical Measurement Systems (CPMS) paradigm. The case study concerns distributed measurement systems based on time-domain reflectometry (TDR), applied to the detection of water and variations in the dielectric properties of the monitored medium. The research unit of the Polytechnic University of Bari contributes primarily to the development of the digital twin of the measurement system, through models capable of reproducing the behaviour of distributed TDR systems under real and non-ideal conditions, and of supporting TDR inversion, simulation and uncertainty evaluation.

Purpose

The main objectives of the research unit of the Polytechnic University of Bari concern:

1. construction of digital twins of distributed TDR systems under real conditions;

2. electromagnetic and computational modelling of the measurement system;

3. development of techniques for the automatic inversion and interpretation of reflectograms;

4. use of the CPMS for measurement-system simulation and uncertainty evaluation;

5. application to the distributed monitoring of water and moisture.

Expected results

The project involves digital twins, electromagnetic and computational models, TDR inversion techniques and algorithms for the automatic processing of reflectograms. Methodologies for the quantitative evaluation of uncertainty, experimental validation in the laboratory and methodological results useful for future CPMS applications are also envisaged.

Achieved results

The results achieved can be summarised as follows:

1. A digital twin of a TDR system based on a two-wire sensor was developed for the quantitative estimation of the soil moisture profile. The model integrates the relationship between soil moisture and dielectric constant, the transverse electromagnetic behaviour of the sensor and the longitudinal behaviour of the sensing line.

2. A TDR inversion methodology based on grey-box models was developed, capable of reproducing reflectograms measured under non-ideal operating conditions, including in the presence of irregular signals and incomplete knowledge of the system's electromagnetic parameters.

3. The digital twin was used for the realistic simulation of the measurement system and for the quantitative evaluation of uncertainty through Monte Carlo simulations representative of variations in the physical system.

4. A two-stage TDR inversion procedure was introduced to improve the accuracy of water localization along the distributed sensor, combining a flexible reconstruction of the dielectric profile with a parametric estimation of the perturbed region.

5. A data-driven approach to TDR inversion based on transformer neural networks was developed for the automatic reconstruction of distributed capacitance profiles from reflectograms, without iterative optimization procedures.

6. The methodologies developed were validated through simulations and laboratory experimental campaigns. The results confirm the effectiveness of the approach for water localization, moisture estimation, and the metrological evaluation of distributed TDR systems.

Expected results

The project involves digital twins, electromagnetic and computational models, TDR inversion techniques and algorithms for the automatic processing of reflectograms. Methodologies for the quantitative evaluation of uncertainty, experimental validation in the laboratory and methodological results useful for future CPMS applications are also envisaged.

Achieved results

The results achieved can be summarised as follows:

1. A digital twin of a TDR system based on a two-wire sensor was developed for the quantitative estimation of the soil moisture profile. The model integrates the relationship between soil moisture and dielectric constant, the transverse electromagnetic behaviour of the sensor and the longitudinal behaviour of the sensing line.

2. A TDR inversion methodology based on grey-box models was developed, capable of reproducing reflectograms measured under non-ideal operating conditions, including in the presence of irregular signals and incomplete knowledge of the system's electromagnetic parameters.

3. The digital twin was used for the realistic simulation of the measurement system and for the quantitative evaluation of uncertainty through Monte Carlo simulations representative of variations in the physical system.

4. A two-stage TDR inversion procedure was introduced to improve the accuracy of water localization along the distributed sensor, combining a flexible reconstruction of the dielectric profile with a parametric estimation of the perturbed region.

5. A data-driven approach to TDR inversion based on transformer neural networks was developed for the automatic reconstruction of distributed capacitance profiles from reflectograms, without iterative optimization procedures.

6. The methodologies developed were validated through simulations and laboratory experimental campaigns. The results confirm the effectiveness of the approach for water localization, moisture estimation, and the metrological evaluation of distributed TDR systems.

PRIN 2022 CALL. DECREE NO. 104 OF 2 FEBRUARY 2022

PROJECT TITLE: Digital Twin Technology for the Development of LP-DI Hydrogen engine for heavy-duty applications (DHYCE-HD)

CODICE CUP: D53D23004150006

Politecnico di Bari Budget: € 148,702

Research Unit Coordinator: Prof. Pietro De Palma

Other Research Units: Polytechnic University of Milan (Prof. Gianluca D’Errico)

Brief project description

The DHYCE-HD project (Digital Twin Technology for the Development of LP-DI Hydrogen Engine for Heavy-Duty Applications) was conceived to accelerate the transition towards zero-emission propulsion systems through the development of an advanced numerical framework dedicated to hydrogen-fueled internal combustion engines.

The project is divided into distinct Work Packages (WPs), which have fostered collaboration between the two research groups of the Polytechnic University of Milan and the Polytechnic University of Bari. The PoliMI group is primarily responsible for developing a reliable numerical approach that will form the backbone of the Digital Twin. The research activities have focused on the development and integration of advanced 1D and 3D CFD simulation tools, in particular the Gasdyn and LibICE codes. These tools have been enhanced to address the distinctive and complex characteristics of hydrogen combustion, such as its extremely high flame speed, low ignition energy, and wide flammability range.

The Polytechnic University of Bari is responsible for implementing non-oscillatory numerical methods (WENO). These high-order accuracy schemes are essential for capturing complex shock waves and discontinuities typical of high-speed hydrogen jets, significantly improving the robustness and accuracy of the simulations.

The main research activity consists of the parallel study of a heavy-duty engine equipped with direct hydrogen injection (H₂-DI). This analysis is conducted at several operating points, comparing established modeling approaches with the newly developed methods. Finally, the entire numerical framework is subjected to a rigorous validation process using experimental data available in the literature, in order to ensure that the predictive capabilities of the DHYCE-HD platform are accurate and reliable for industrial applications.

Objectives

1. Identification of the main advantages and disadvantages of a low-pressure-direct injection (LP-DI) hydrogen engine for heavy-duty applications.

2. Strengthening of know-how relating to hydrogen engine technology, with particular reference to: a) the complex physical processes involved in the direct injection of hydrogen into engines and the subsequent formation of the mixture; b) the effects of H₂ injection pressure and flow rate, as well as injector position and geometry, on engine efficiency and emissions; c) the turbulent combustion of premixed air–hydrogen mixtures, including stratified conditions, with the determination of the lean combustion limit and knocking risk; d) the formation of nitrogen oxides inside the cylinder and strategies for minimising them;

3. Development of fast and accurate CFD methodologies for application to direct-injection (DI) hydrogen engines, based on open-source technologies accessible for future research activities;

4. Definition of a virtual LP-DI hydrogen engine configuration for heavy-duty applications, characterized by high efficiency and low NOx emissions.

Main expected results:

1. Preparation of an up-to-date report on the state of the art regarding the use of hydrogen as a fuel in internal combustion engines;

2. Definition of correlations for laminar flame speed to be used in CFD codes and 1D models;

3. Acquisition of knowledge regarding the fundamental aspects of hydrogen injection, mixing, and combustion, including the assessment of high-accuracy numerical schemes;

4. Definition of an accurate numerical methodology specifically developed for hydrogen LP-DI engines;

5. Evaluation of the performance, fuel consumption and emissions of a turbocharged and optimized virtual hydrogen LP-DI engine under both steady-state and transient operating conditions;

6. Evaluation of the influence of the start of injection timing and ignition advance on pre-ignition and NOx emissions under lean-combustion conditions;

7. Definition of a digital twin of the combustion chamber of a hydrogen LP-DI engine, demonstrating the feasibility of breakthrough performance in terms of efficiency and emissions using a carbon-free fuel;

8. Production of detailed technical information for the development of a “green” hydrogen engine, with significant economic and social impacts in the mobility and energy sectors in Europe and Italy, within the context of the ecological transition towards a sustainable economy.

Results obtained

Significant results were achieved by the research unit (RU) of the Polytechnic University of Bari, which developed and validated high-fidelity numerical methodologies for hydrogen injection and mixing. In particular, the implementation of high-order WENO schemes enabled the accurate resolution of underexpanded hydrogen jets, capturing complex shock structures, hydrogen-jet instabilities and mixing dynamics with greater accuracy than conventional schemes. These developments represent a significant advancement in addressing one of the main challenges of direct-injection hydrogen engines, namely the rapid and efficient air–fuel mixing under highly compressible-flow conditions.

In parallel, analytical correlations were developed for laminar flame speed, together with tabulated kinetic approaches for autoignition, providing robust and computationally efficient models for predicting combustion and knocking in both 1D and 3D simulations.

The validated numerical framework was extended to three-dimensional CFD simulations of the entire engine cycle. A reliable 1D–3D coupling methodology was also developed and validated against experimental data from a hydrogen-fueled engine, demonstrating its ability to accurately reproduce in-cylinder pressure evolution, flow structures and turbulence characteristics. Detailed analyses of mixture formation and combustion processes were also conducted for hydrogen-fueled configurations.

The WENO methodology was successfully applied to full 3D simulations of the internal combustion engine under consideration. Three operating conditions were analysed, characterised by different hydrogen injection start-of-injection timings (SOI = 540, 630 and 660 CAD) at constant injection pressure. This activity enabled a direct assessment of the impact of high-order numerical schemes on the prediction of injection, mixing and in-cylinder flow evolution phenomena under realistic engine operating conditions. The results confirmed the greater ability of WENO-based approaches to capture sharp gradients, shock-wave interactions and stratification effects, thereby improving the predictive reliability of CFD simulations and directly contributing to the development of a high-fidelity digital twin.

Overall, these results provide a sound scientific and technological basis for future developments in the field of hydrogen internal combustion engines and contribute to the advancement of zero-carbon-emission propulsion technologies for the heavy-duty sector.

PRIN 2022 CALL. DECREE NO. 104 OF 2 FEBRUARY 2022

TITOLO DEL PROGETTO: A pioneering Compton Camera for hadrontherapy with a 3D silicon Pixel Chamber

CUP CODE D53D23002810006

Budget: € 86,500

P.I. or U.R. Coordinator  Prof. Giuseppe Eugenio Bruno

Other Research Units University of Cagliari, INFN

Brief project description

The project proposed the development of a new generation of high-performance Compton Cameras, based on the innovative use of a silicon Pixel Chamber as a scattering detector. The objective is to overcome the limitations of traditional Compton Cameras, which require a large number of photons to reconstruct the direction of the gamma source, preventing truly online use in critical applications such as hadron therapy.

The Pixel Chamber was conceived as a three-dimensional continuous-tracking detector, obtained by superimposing numerous ultra-thin monolithic sensors with extremely high granularity. This configuration makes it possible to reconstruct, with micrometre precision, the trajectory of the recoil electron produced in the Compton interaction. Information on the tracking of the Compton electron makes it possible to significantly narrow the volume of uncertainty regarding the direction of the incident photon, reducing the reconstruction from an entire conical surface to a small cone segment, with a potential increase in imaging efficiency of several orders of magnitude.

Purpose

The project demonstrated the possibility of assembling and operating the A9, the basic unit of the Pixel Chamber consisting of a stack of 9 ultra-thin silicon ALTAI or ALPIDE chips, as conceived in the original proposal. Studies on the mechanical and electrical integration of several A9 cells to obtain volumes suitable for achieving high efficiency values for Compton interaction in the scatterer of the Pixel Chamber revealed implementation challenges when using chips with dimensions on the order of ~ 1 cm2 (the typical “field of view” dimensions of the lithographic techniques used by the foundries producing monolithic chips, such as ALPIDE or ALTAI). The possibility, thanks to the new “stitching” technique, of producing chips with much larger dimensions, up to the limits imposed by the size of the silicon wafer itself (e.g. with a diameter of 300mm in the 68nm TPSCo CMOS technology, chips measuring 10x10 cm^2 can be readily obtained

and even up to 15x15 cm^2), with all the electrical interconnections for readout and power supply located at the edge of the chip itself, would resolve the implementation challenges. With the adoption of this solution, the only critical aspect remaining is the dissipation of the heat produced: it is therefore very important to reduce power consumption per unit of area, especially when aiming to produce a stack of approximately ten large chips. Accordingly, in addition to focusing on ALTAI chips, the chip characterisation campaign was extended to the development and characterisation of new test structures and the first prototypes for large chips in 68nm CMOS technology. The results obtained demonstrated the possibility of reducing power consumption to 35 mW / cm. 

Expected results

An in-depth thermal analysis was conducted on the scatterer, consisting of a 4x3 array of Pixel Chambers equipped with interdigitated heat sinks, using COMSOL simulations. The study showed that forced-air ventilation is sufficient and that the addition of external heat sinks further improves heat dissipation, especially for ventilation speeds ≥ 2 m/s, making it possible to maintain the scatterer within the operating range of 40-45 °C. 

Achieved results 

As part of the project, a complete simulation of the Pixel Chamber was implemented in GEANT4, including the detailed geometry of the sensor stacks with their respective alignments and inter-chip gaps. The simulation used a 1 MeV photon source, an energy representative of the range of interest for hadron therapy applications. Events useful for reconstruction were selected by requiring a single Compton interaction within the active volume and at least 10 detected track points from the recoil electron. The electron direction was reconstructed using a linear fit to the Compton interaction point and the first 3 subsequent hits along the track; this choice was motivated by the need to limit the impact of multiple scattering, which is more pronounced in the distal regions. The results obtained show a mean value of the

angular deviation of 15.7° and an RMS of 14.1°, with a distribution consistent with a significant component of well-reconstructed low-angle events. The main limiting factors identified are multiple Coulomb scattering of the recoil electron, pixel granularity, and the limited track length available for the fit. These results provide an initial quantitative benchmark of the proposed geometry and offer concrete guidance for optimizing the Pixel Chamber design in preparation for future developments.

PRIN 2022 CALL. DECREE NO. 104 OF 2 FEBRUARY 2022

TITOLO DEL PROGETTO: Design of Acoustic Metamaterials for Aeronautical applications (DAMA)

CODICE CUP: D53D23003270006

Budget: € 81.113

Dr. Maria Cinefra

Other Research Units or any Sub-Units:

• Polytechnic University of Turin; 

• University of Naples Federico II.

Project Overview

The project “Design of Acoustic Metamaterials for Aeronautical Applications (DAMA)” focuses on the development of advanced numerical and experimental methodologies for designing innovative acoustic metamaterials aimed at reducing noise and vibrations in aircraft cabins. The research adopts a multidisciplinary approach combining structural dynamics, vibro-acoustics, advanced numerical modelling, and experimental validation, with the goal of improving the vibro-acoustic performance of lightweight and tunable solutions for aerospace applications.

Objectives

The main objective of the project is to enhance acoustic comfort in aircraft cabins through the design of high-performance metamaterials. This includes:

• modelling the vibro-acoustic response of aircraft structures;

• developing advanced numerical tools for complex metamaterial geometries;

• designing lightweight and tunable structures capable of controlling wave propagation;

• validating the proposed solutions through experimental testing.

Expected Results

The project initially aimed to:

• establish a robust numerical framework for the analysis of complex vibro-acoustic systems;

• identify innovative metamaterial configurations with enhanced acoustic properties;

• demonstrate improved transmission loss and vibration reduction compared to traditional solutions;

• validate numerical predictions through experimental campaigns;

• provide a foundation for future large-scale applications in aeronautical structures.

Achieved Results

The project successfully delivered significant advancements in both modelling and experimental validation. In particular:

• a comprehensive numerical framework based on the Carrera Unified Formulation and adaptive finite elements was developed;

• origami-based metamaterials (notably Miura-ori structures) were identified and optimized for tunability and acoustic performance;

• vibro-acoustic models, including Rayleigh integral formulations, were implemented for non-planar structures;

• experimental tests confirmed the effectiveness of the proposed metamaterials, showing improved acoustic attenuation and strong dependence on geometric parameters;

The results demonstrated the feasibility of designing lightweight, tunable acoustic metamaterials for aerospace applications, providing a solid basis for future developments and full-scale integration.

BCALL PRIN 2022D. D. N. 104 DEL 2 FEBRUARY 2022

PROJECT TITLE: A capability-based decision support framework for circular economy transition

CODICE CUP: D53D23006650006 

Budget: € 118.666 

P.I. or U.R. Coordinator  Ilaria Filomena Giannoccaro (Polytechnic University of Bari) 

Other Research Units or any Sub-Units: Polytechnic University of Milan, LIUC Università Carlo Cattaneo

Project objectives

How can companies effectively develop, implement and monitor their Circular Economy (CE) strategy? Despite the growing body of scientific literature on the subject in recent years, very few studies have addressed this issue from a strategic perspective. 

The aim of the project was to fill this gap by developing an integrated decision-making tool capable of supporting companies in their transition towards the Circular Economy by addressing three key phases: 1) the strategic phase, suggesting the most effective CE strategies for companies to adopt; 2) the planning phase, identifying the management practices to be implemented to put the selected CE strategies into practice; and 3) the control phase, proposing specific indicators to monitor progress and the results achieved overall. The capabilityapproach was adopted to develop the decision-making tool. The main results of the project are reported below. 

With reference to the strategic phasestrategic phase , the research group developed two tools based on the theory of economic complexity: (i) theCountry Circular Economy Capability Space , aimed at identifying the specific CE capabilities possessed by companies in a given country, and (ii) theCircular Economy Capability Proximity Matrix planning phase, aimed at identifying the most effective CE strategies that companies should adopt in a specific economic sector based on their CE capabilities. The application of these tools to the input-output tables (49 countries and 163 industries) extracted from EXIOBASE highlighted significant differences in the distribution, variety and transferability of circular capabilities across countries. The results also demonstrated that CE strategies cannot be considered implementable by all industries, since their effective adoption depends on the degree of correlation with the capabilities already embedded in a given sector. With reference to the control phaseplanning phase Circular Measurement Toolkit, the research group identified 82 relationships between management practices, CE strategies and company capabilities and developed a new maturity model to support the development of new capabilities for implementing circular economy strategies in sectors considered particularly critical. With reference to the control phase, the research group developed a system for measuring circularity performance. Starting from the identification of the main CE performance indicators, considering different aspects of circularity, such as resource-use efficiency, the sustainability of the materials used and design oriented towards durability and closed-loop systems, a framework was developed, capable of meeting companies’ need to measure their level of circularity, and a framework for Circular Balanced Scorecard, specifically adapted to circular contexts, as it is entirely founded on managerial practices for circular business models. The integrated decision-making tool has finally been applied to multiple case studies to test its effectiveness. The empirical evidence collected from three Italian companies operating in the textile and clothing sector has allowed for the testing of the integrated decision-making tool and the development of guidelines for its use by future users. 

The results provide scientific and managerial contributions. They help bridge the critical gap between the formulation and implementation of EC strategies by developing an integrated decision-making tool that explains the selected EC strategies based on the companies' capabilities, the managerial practices to be implemented, and the EC performance indicators to be used to monitor and control the implementation of the selected EC strategies. The academic literature is also enriched by two tools of economic complexity, namely the , the research group developed two tools based on the theory of economic complexity: (i) the and the , aimed at identifying the specific CE capabilities possessed by companies in a given country, and (ii) the and the Circular Measurement Toolkit and the Circular Balanced Scorecard which are effective in identifying which EC strategies are most consistent with the existing capabilities of specific sectors and national production systems; and by two measurement frameworks, namely the

and the which are effective in identifying, monitoring, and controlling the effectiveness of managerial practices for EC strategies concerning financial and environmental outcomes. Managers can benefit from the main outcome of the project, namely the integrated decision-making tool, particularly for selecting EC strategies aligned with the existing capabilities of the enterprise, and for appropriate managerial practices and performance indicators to enable, monitor, and control the implementation of the selected EC strategies. This reduces the risk of adopting unfeasible or inconsistent strategies with the enterprise's production structure, thus lowering implementation costs, improving resource allocation, and increasing the likelihood of success in the transition to EC. In this way, the developed tool supports companies in enhancing their capacity to compete in increasingly sustainability-oriented markets. Policymakers can also benefit from the developed tool. It suggests prioritizing targeted interventions in lagging sectors to accelerate capability development while simultaneously strengthening those sectors that already possess solid EC capabilities, namely the leading sectors. This provides a useful foundation for designing more effective EC interventions at the national level, capable of producing more significant impacts on the environmental and economic sustainability of the entire economic system., The results of the project have been widely disseminated through, 5 scientific journal publications, 2 submitted scientific articles to peer-reviewed journals, 5 publications in international conference proceedings e 2 publications in national conference proceedingsand also through the organization of a workshop and a thematic track within the framework of an international conference.

The project has generated social impacts in terms of public engagement and dissemination of knowledge to a wide audience, including stakeholders from the academic and industrial sectors. Consistent with the project plan, this has been made possible through the publication of the organization of teaching sessions in postgraduate courses, a dedicated webinar of the project ( Massive Online Open Course and the website of the project (https://circulareconomycapabilit.altervista.org/). 

BCALL PRIN 2022D. D. N. 104 DEL 2 FEBRUARY 2022

PROJECT TITLE: "ARCHITA – ARCHaeological and ARCHitectural Integrated platform for Taras”

CODICE  CUP: D53D23000880006

Budget totale: € 77.047

Polytechnic University of Bari (RdU3): project coordinator Prof. Monica Livadiotti

Research staff: Antonello Fino (RTD-A researcher); Chiamaria Spinelli (research fellow recruited by the project); Roberta di Bari (PhD student); Carmine Chiumarulo (contract researcher hired by the project); Gianluca Ranieri (contract researcher hired by the project).

Unitelma-Sapienza, Rome (RdU1): project coordinator Prof. Rita Sassu (P.I.) 

Research staff: Davide Falco (research fellow recruited by the project); Giulia Vannucci (research fellow recruited by the project).

Sub-Unit: National Underwater Superintendency of Taranto, represented by archaeologist Dr Roberto Rotondo 

E-Campus University (RdU2): project coordinator Prof. Riccardo Pecori (Deputy P.I.)

        Research staff: Luca Liparulo (RtdA researcher recruited by the project)

Description and expected results: 

The “ARCHITA – ARCHaeological and ARCHitectural Integrated platform for Taras” project aims to deepen knowledge of ancient Taras through an interdisciplinary approach encompassing archaeology, topography, architectural history and computer engineering. With the support of the National Superintendency for Underwater Cultural Heritage of Taranto (project partner), which has granted access to the archives and monuments under its jurisdiction, the project will undertake the systematic mapping, classification and analysis of the architectural remains of the ancient settlement. The data will be entered into an archaeological map based on a GIS platform. The project will therefore advance the state of the art, contributing to the reconstruction of Taras’s urban landscape in its various phases. The map and the GIS database will be developed jointly by UdR1 (Unitelma – Sapienza University of Rome) and UdR 3 (Polytechnic University of Bari). In this context, RdU2 (eCampus University) will develop an AI-ready digital platform and mobile applications designed to facilitate the analysis and dissemination of archaeological data. The mobile app will also be intended for the general public, who will be able to explore Taranto’s archaeological heritage through thematic routes and interactive itineraries. The ancient city of Taranto is not easy to define, partly because of the major transformations it underwent from the end of the nineteenth century onwards, when the rapid growth of the Borgo district based on the Conversano Master Plan led to an urban transformation that often erased traces of the past. The platform may therefore become a reference model for the effective multilevel organisation and management of data, potentially suitable for adoption by other cultural institutions. 

Results achieved: 

The ARCHITA project has achieved its main objectives, particularly in the areas of digitalisation, innovation, culture and tourism, as well as education and research. The research activities were structured around four thematic activities:

Activity 1. Analysis of the urban development of ancient Taranto from its foundation to the medieval period (RdU1 and RdU3): The project enhanced knowledge of Taranto’s urban development through the analysis and systematic organization of archaeological, archival, and bibliographic documentation. The archive documentation of the Superintendency was digitized and georeferenced, creating a comprehensive database and a CAD map of Taras, serving as a basis for reconstructing the urban layout in its various phases. Analysis of the archaeological contexts and architectural remains made it possible to produce maps illustrating the diachronic transformations of the settlement.

Activity 2. Collection of archaeological data and implementation of the GIS (RdU3, RdU1, RdU2): Building on the data collected during Activity 1, the project developed an advanced GIS environment designed to manage and integrate the large volume of data relating to the ancient city. The system enables the correlation of archaeological structures belonging to the same chronological phases and facilitates the automatic production of thematic and diachronic maps. The GIS also constitutes a valuable tool for heritage management and preventive archaeology, supporting more sustainable urban planning and conservation strategies.

Activity 3. Design and development of the AI-based platform and user interface applications (RdU2): UdR2 developed a mobile application that enables specialists and the public to access information through an intuitive interface, allowing them to explore Taranto’s heritage through thematic routes and interactive itineraries. 

Activity 4. Testing and dissemination within the framework of public archaeology (all research units): The project also achieved significant results in the field of dissemination and public engagement. The digital tools developed were presented through a series of outreach initiatives, including public conferences, seminars open to students and the community at large, and educational activities designed to engage different types of audiences.

By combining humanities research with digital technologies and public engagement, ARCHITA thus fostered cooperation among academia, public institutions, and local communities, contributing to the protection, conservation, and enhancement of Taranto’s cultural heritage.

Dissemination and publication of the results obtained

Website: to ensure the widest possible dissemination, two different websites were created: one official website dedicated to the project as a whole (https://archita-project.it/) and another specifically dedicated to the research conducted by UdR 3 of the Polytechnic University of Bari (https://Archita2022.it)

Conferences and Lectures: Dissemination activities began with the Kick-off Meeting (Taranto, Superintendency headquarters, 20-03-2024) and concluded with the final conference entitled “A City Between Two Seas” (Bari, ArCoD Department, 20-02-2026), at which the main results of the project were presented.

Publication of the results: The scientific results will be published in the Class A, open-access, online scientific journal Thiasos (www.thiasos.eu), for which a final monograph is currently being prepared. Some preliminary results of UdR3 have already been presented in scientific articles. See: 

- M. Livadiotti, G. Rocco, The Doric Temple beneath San Domenico in Taranto, in R. Belli, R. Sassu (eds.), In the Sign of Archytas. Writings in Memory of Enzo Lippolis, Thiasos Monographs 20, Rome 2023, pp. 45-78; 

- C. Spinelli, R. Di Bari, Reconstructing Taranto: the archaeology of drawing between urban memories and stratified archives, in G. Fioretti (ed.), 3rd Congress on Cultural Heritage in Puglia, 7–10 October 2025, forthcoming.

BCALL PRIN 2022D. D. N. 104 DEL 2 FEBRUARY 2022

PROJECT TITLE SUPERcrystals by ligand-controlled organization of colloidal nanoparticles (SUPERNANO)

CUP CODE D53D23005380006

Budget: € 83.864

P.I. or U.R. Coordinator 

Prof. Roberto Grisorio

Other Research Units or any Sub-Units

National Research Council (CNR)

University of Naples “Federico II” 

BREVE DESCRIZIONE DEL PROGETTO

The SUPERNANO project focuses on the field of nanomaterials and aims to develop three-dimensional supercrystals (SCs) obtained through the controlled self-assembly of colloidal nanoparticles, particularly quantum dots and perovskite nanocrystals. The objective is to overcome the difficulty of integrating nanoparticles into devices by creating micrometre-scale structures that retain the optical and electronic properties typical of the nanoscale. SCs are systems in which nanoparticles behave as “artificial atoms”, giving rise to emerging collective properties. The project combines i) controlled nanoparticle synthesis, ii) surface-ligand engineering, iii) self-assembly techniques and iv) advanced characterisation and multiscale modelling.

PURPOSES

The main purposes of the project concern i) the fabrication of highly ordered 3D supercrystals from luminescent nanoparticles, ii) control of the interaction between nanoparticles through ligand design, reducing interparticle distances, iii) improvement of the stability and reproducibility of the superstructures, iv) electronic coupling between nanoparticles to achieve advanced collective properties and v) the development of functional materials for optoelectronic and photonic applications.

A key aspect is the engineering of surface chemistry to move from weakly coupled systems to structures with strong electronic interaction and state delocalisation.

EXPECTED RESULTS

The project aims to achieve:

• stable and highly ordered supercrystals, with control over morphology and crystal phase,
• reduction of the distance between nanoparticles to the sub-nanometre regime,
• emergence of collective properties (e.g. dipolar coupling, electronic minibands),
• improvement of optical properties (emission, efficiency, spectral control),
• development of predictive multiscale modelling for materials design.

These results are fundamental for applications in:

• LEDs and lasers
• optoelectronic devices
• advanced photonics
• quantum technologies

RISULTATI RAGGIUNTI

The experimental activity has led to significant results in the understanding and fabrication of supercrystals based on perovskite nanocrystals:

• Development of a controlled self-assembly strategy based on slow destabilisation in the presence of an anti-solvent, enabling the direct formation of well-defined micrometre-scale supercrystals,

• Demonstration of the role of Mn(II) doping in modifying the optical properties (introduction of a red-orange emissive band), morphology and assembly dynamics,

• Observation of complex hierarchical structures, with non-emissive bulk-like internal domains and peripheral regions that remain nanocrystalline and luminescent,

Overall, these results demonstrated that doping and surface chemistry profoundly influence self-assembly and that it is possible to design superstructures with tailored optical and structural properties.