Quantum Sciences and Nanotechnologies

Hybrid spin-superconducting systems constitute an ideal playground for exploring quantum effects and to approach Quantum Technologies.

A first thesis project, in co-tutoring with Dr. C. Bonizzoni, deals with the development and the implementation of advanced protocols (i.e. microwave and/or radiofrequency pulses sequences) for initializing, manipulating and reading out molecular spin qubits at ultra low temperature (down to mK) also in combination with machine learning methods [1-6].

With a second thesis project, in co-tutoring with Dr. A. Ghirri (CNR-NANO), we propose to experimentally get spectral control of spin excitations (magnons) in ferromagnets [7-8]. 

Alternatively, we propose to develop and use optical detected magnetic resonance technique for the implementation of quantum sensors based on colour defects in diamonds.

References:
[1] Adv. Phys. X 3,1435305 (2018)
[2] npj Quantum Inf. 6,68 (2020)
[3] Phys. Rev. Appl. 18, 064074 (2022)
[4] npj Quantum Inf. 10, 41 (2024)
[5] https://doi.org/10.1038/s41467-025-67163-z
[6] https://doi.org/10.1103/56hv-qp5d
[7] Phys. Rev. Appl. 20, 024039 (2023)
[8] Phys. Rev. Appl. 22, 034004 (2024)

Modern Transmission Electron Microscopes (TEMs), such as the SPEQTEM recently installed at UNIMORE, provide us with the opportunity to visualize nanoscale processes in real-time. Those include the electric and magnetic fields that are underpinning the functionality and performance of technological devices. Quantum Materials are the new frontier of science and technology. Coherent Electron Imaging is the key to visualize and understand this vastly unexplored territory, which is crucial to make progress towards a large scale implementation of Quantum Technologies.

There are several opportunities for a PhD in this exciting field of science, which sits at the boundary between disciplines such as condensed matter physics, chemistry, geology, material sciences and biology:

1) Electron phase plates for beam shaping
To improve the performance of Coherent Electron Imaging, the manipulation of the electron phase is essential. This is achieved by electron-optical devices called “phase plates”, that, once properly designed and fabricated, allow almost-arbitrary operations on the electron wave function [1].

2) Electron-photon entanglement
Entanglement is perhaps the quintessential quantum phenomenon. We want to realize and use an electron-photon entangler for the TEM, which may soon lead to a hybrid electron-photon microscope that combines the unique powers of the two quantum probes [2].

3) Electron beam chemistry
Electron irradiation, often seen as a source of damage in microscopy, can also be exploited beneficially. This is the concept of functional electron beams, where the beam is turned into a fine scalpel that drives nanoscale physical-chemical reactions and produces patterns with a desired functionality [3].

UNIMORE is at the forefront of these fields, and PhD candidates with interest in exploring the Quantum World with electrons are most welcome to apply with us.

References
[1] “Generation of electron vortex beams with over 1000 orbital angular momentum quanta using a tunable electrostatic spiral phase plate”. A.H. Tavabi, P. Rosi, A. Roncaglia, E. Rotunno, M. Beleggia, P.-H. Lu, L. Belsito, G. Pozzi, S. Frabboni, P. Tiemeijer, R.E. Dunin Borlowski, V. Grillo. Appl. Phys. Lett. 121, 073506 (2022).
[2] Project Two to Tango, EIC-Pathfinder 2026, under review
[3] “Effect of molecular weight on the feature size in organic ice resists”. A. Elsukova, D. Zhao, A. Han, M. Beleggia. Nanoletters 18, 7576-7582 (2018).

Quantum features of physical systems offer new opportunities to enhance the generation, distribution, and storage of information across large-scale networks. This project aims to explore quantum walks as a framework for modeling quantum information networks, focusing on quantum superpositions, interference, chirality, and next-nearest-neighbor interactions as potential resources for improved transmission, routing, and storage. Additionally, we will investigate scenarios in which noise, contrary to expectations, can enhance information protocols without compromising quantum advantages. Beyond applications in quantum communication and computation, we plan to extend these findings to more general quantum-inspired technologies, including link prediction in biological and financial networks, as well as source localization in imaging and epidemiology.

Collaborations: M.G.A. Paris, Dipartimento di Fisica, Università degli studi di Milano.

References:

[1] Entropy 27, 498 (2025)
[2] Phys. Rev. A 111, 032439 (2025)
[3] J. Phys. Complex. 7, 025001 (2026)

Quantum science and technology (QS&T) are driving transformative advances in industry and society by introducing disruptive innovations in computational methods, materials/drug design, communication, and sensing.

The core components of QS&T are quantum bits (qubits), which are often created in solid-state materials by a top-down approach. However, the electronic spin of certain paramagnetic metal-organic complexes also displays highly coherent dynamics and can be probed and controlled by magnetic resonance techniques, in much the same way as NMR does with nuclear spins, but on a much faster timescale [1,2]. This molecular approach to electronic spin qubits may bring strong benefits, such as atomically precise design by molecular chemistry, control over qubit-qubit interactions, and scalability. Challenges in the field are the design of new molecular qubits with enhanced properties and their controlled organization into 2D or 3D arrays by deposition on surfaces [3] or insertion in nonmagnetic crystal lattices [4], including those of metal-organic frameworks (MOFs).

Proposed sub-topics:
– Boosting quantum coherence in molecular spin qubits by coordination chemistry;
– Qubits-on-MOFs for quantum sensing applications.

Collaborators: prof. R. Clérac (CRPP-CNRS, Pessac, France), prof. G. Aromí, dr. V. Novikov (Dept. of Inorganic and Organic Chemistry, Univ. of Barcelona), prof. M. Affronte, dr. C. Bonizzoni (FIM, UniMORE), prof. M. Mannini (DICUS, UniFI).

Key techniques: molecular synthesis, X-ray diffraction, magnetic measurements, EPR spectroscopy.

References:
[1] E. Coronado, “Molecular magnetism: from chemical design to spin control in molecules, materials and devices”, Nat. Rev. Mater. 5, 87 (2020)
[2] M. Imperato et al., “Quantum spin coherence and electron spin distribution channels in vanadyl-containing lantern complexes”, Inorg. Chem. Front. 11, 186 (2024)
[3] N. Giaconi et al., “Deposition of an addressable molecular spin qubit with built-in decoupling structure”, J. Am. Chem. Soc. DOI: 10.1021/jacs.6c01396
[4] M. Lanza et al., “Quantum sensing of time-dependent magnetic signals with molecular spins”, Phys. Rev. Applied 25, 034045 (2026).

Nanostructured materials are at the forefront of next-generation green technologies thanks to their unique physical properties and potential in fields such as photocatalysis, plasmonics, optoelectronics, and environmental sensing.

A central activity concerns the fabrication of metal and core–shell nanoparticles physically synthesized by a gas aggregation source of nanoclusters, and by reactive MBE. The research will investigate both the properties of individual nanoparticles and the collective behavior in nanoparticle-assembled films. Studies will be devoted to the ultrafast dynamics of photoexcited states in oxide-based materials combined with plasmonic nanoparticles. Through ultrafast spectroscopies, also exploiting X-rays, the goal is to enhance visible and near infrared light harvesting efficiency and to promote the formation of long-lived excited states, crucial for the development of highly efficient photocatalysts or sensors.

Another field is the exploration of transparent conductive oxides (TCO) because of their plasmonic applications in areas like nanophotonics and gas sensing, to substitute noble metals for integration in CMOS devices. TCOs combine low resistivity and high transparency with a plasmon resonance that can be tuned from the VIS to mid-IR range leading to peculiar photonic properties both in films and nanostructures.

The systems will be characterized by experimental techniques (XPS, SEM, XRD, transport characterization, AFM, optical spectrophotometry) at the FIM dept, in collaboration with CNR-NANO, national and international research groups, at large-scale research facilities and with theoretical scientists.

References:
[1] S. Pelatti et al. “Ultrafast Dynamics of Electronic and Structural Modifications Induced by Photoexcitation in Cerium Oxide”, Adv. Electron. Mater, 11, e00429 (2025). doi: 10.1002/aelm.202500429
[2] T. Virgili et al. “Tailoring the Ultra-Fast Infrared Optical Response of Al:ZnO Through Nanostructuration”, Adv. Mater. Technol. 2025, 11, e01940; doi: 10.1002/admt.202501940.

Co-Tutors: Dr. Giovanni Bertoni, Dr. Enzo Rotunno (CNR-NANO)

Research Objective:

This PhD project aims to advance the nanoscale characterization of materials for energy applications—including nanostructured semiconductors, oxides, and Li-based storage materials—through an integrated approach combining high-resolution transmission electron microscopy (TEM) and artificial intelligence (AI). The project will leverage cutting-edge instrumentation (SPEQTEM, a high-energy resolution monochromated TEM) and machine learning to automate and enhance both data acquisition and analysis workflows.

Main Research Activities:

1. AI-Assisted Multislice Simulations: Development of an on-the-fly, AI-enhanced Multislice simulation framework for predicting TEM images with quantitative structural and chemical contrast, incorporating inelastic scattering contributions. These simulations will guide experimental interpretation and support model validation.

2. ML-Driven STEM-EELS Analysis: Implementation of machine learning algorithms for the automated analysis of STEM-EELS hyperspectral datasets. Goals include simultaneous optical and chemical mapping, phase recognition, and pixel-wise segmentation to extract relevant information on local material properties.

3. Automation of Operando TEM Workflows: Design and deployment of automated acquisition protocols for ex situ and in situ/operando TEM experiments. This includes reducing beam damage and enhancing throughput in dynamic experiments on energy materials (e.g., Li/Na batteries, nanocatalysts, nanoplasmonics). The integration of AI into TEM workflows will significantly increase efficiency and reproducibility while enabling quantitative and correlative analysis of complex functional materials at the atomic scale. The results will contribute to materials discovery and optimization for next-generation energy technologies.

Collaborations: CNR-NANO; access to SPEQTEM and AI resources from the iENTRANCE@ENLinfrastructure.

Key References:

1. SPEQTEM: https://tem-s3.nano.cnr.it/

2. Ultramicroscopy 245, 113663 (2023)

3. JACS 144 (8), 3442–3448 (2022)

4. ACS Materials Lett. 1 (6), 665–670 (2020)

Spin-orbit coupling plays a crucial role in nano-materials by enabling the control of electronic spin through structural design, thereby underpinning advanced applications in spintronics, topological quantum devices, and low-power information technologies. Often, multiscale modeling is essential, requiring the integration of diverse computational methods, from atomistic to continuum approaches. Several directions of research are envisaged in this doctoral activity: 

1 – thermoelectricity in hetero-structured nanowires is a fascinating viable perspective for compact, scalable solution for heat-to-electricity conversion, enabling self-powered devices and sensors. Our state-of-the-art continuum methods (AB) may combine with atomistic simulations (AR) and confront with experimental activity (FR) to identify materials and new material designs. 

2 – Spin-charge interconversion in topological materials may enable efficient manipulation and detection of spin signals using electrical means, paving the way for faster, low-power spintronic devices (AB). 

3 – Spin qubits in Si/Ge-based quantum dots involves a multi-scale approach with simulation of spin-orbit coupled dynamics of (possibly new) qubit encodings (FT), description of material-specific decoherence mechanism (FG), and collaboration with experimental partners.

In collaboration with / possible co-supervisor: (AR) A Ruini, FIM-UNIMORE; (FR) F Rossella, FIM-UNIMORE; (AB) A Bertoni, CNR-NANO; (FG) F Grasselli, FIM-UNIMORE; (FT) F Troiani, CNR-NANO

References:
Spin-orbit control of Dirac points and topological end states in inverted gap nanowires under a transverse electric field, A Vezzosi, A Bertoni, M Gibertini, G Goldoni, Phys Rev B 112, 085425 (2025)

InP/GaSb core-shell nanowires: A novel hole-based platform with strong spin-orbit coupling for full-shell hybrid devices, A Vezzosi, C Payà, A Bertoni, G et al, SCIpost Physics 18, 69 (2025)

Spin-Resolved Magneto-Tunneling and Giant Anisotropic g-Factor in Broken Gap InAs-GaSb Core-Shell Nanowires, V Clerico, P Wojcik, A Vezzosi, A Bertoni, G Goldoni, G F Rossella, et al, NANO LETTERS 24, 790 (2024)

Quantum estimation and remote charge sensing with a hole-spin qubit in silicon, G. Forghieri, A. Secchi, A. Bertoni, P. Bordone, F. Troiani, Physical Review Research 5, 043159 (2023)

Band structure of – and -doped core-shell nanowires, A Vezzosi, A Bertoni, G Goldoni, Phys Rev B 105, 245303 (2022)

We develop and apply first-principles methods to simulate materials for solid-state quantum technologies, focusing on light-driven processes in materials and molecules and on how electromagnetic perturbations excite electrons and nuclei. Simulations use density functional theory (DFT), Green’s function many-body approaches (GW, BSE, quantum embedding), molecular dynamics, and time-dependent DFT. The group has access to CPU/GPU clusters, HPC, and quantum computing facilities. PhD projects span theory and algorithm development, code development, and applications to quantum technologies. Students will collaborate with postdocs, PhD and master’s students, and international partners.

Subtopic A. Simulation of spin defects in silicon nanostructures, silicon carbide, and nitride materials. Key questions include whether silicon can support scalable quantum computing, identifying the microscopic origin of photoluminescence in SiC for room-temperature quantum technologies, and reconciling photoluminescence and X-ray/electron energy-loss experiments in nitride materials to develop robust single-photon emitters. Collaborations with UTorino and BNL/CUNY (USA).

Subtopic B. Development of methods to simulate finite-temperature photoluminescence and optical control of spin polarization in defects and molecular spin centers. Questions include how to compute forces in light-driven out-of-equilibrium systems, embed quantum chemistry methods in DFT to bridge length scales, and use machine learning to accelerate time evolution and capture electron–nuclear correlations. Collaboration with CNR-Nano (Italy).

Subtopic C. Development of algorithms for large-scale electronic-structure calculations on exascale and quantum computers, aiming to extend simulations to unprecedented scales and accuracy. Collaboration with UChicago (USA).

To show interest in any of these topics or to find connections between them, please reach out to Marco Govoni by email.

Innovative nanomaterial-based prototypical device architectures with potential applications encompassing sustainable energy conversion [1,2], sensoristics [3] and nanoelectronics [4] as well as quantum technologies [5] have been recently enabled by iontronics, combining 2-D materials or semiconductor nanowires with soft-matter systems (e.g. electrolytes, biomolecules or bioinspired interfaces) where charged particles (molecules or ions) are free to drift under electrical or thermal biases. These devices are engineered as multi-gate nanotransistors that exploit electrolytes as dielectrics to build up Electric Double Layers (EDLs) at nanoobject-electrolyte interfaces, providing ultra-intense local electric fields that can be tailored to control the transport properties in the nanostructure. To precisely understand and optimize the functional interfaces, a predictive multiscale modelling framework is required. Atomistic Molecular Dynamics simulations can decode the nanoscale structure, [6] charge density and polarization of the EDL, while continuum-level models can bridge these insights to macroscopic device transport properties. Implementing this joint experimental-computational approach, ion-gated and ion-sensitive nanoscale transistors can be developed to enable novel strategies for the sensing of biomarkers and biologically relevant molecular targets, electrochemical devices based on chiral molecules, alternative measures of electrical parameters, novel architectures for thermoelectrics, as well as to engineer low-dimensional density of states in quantum semiconductor nanomaterials. The project will be developed at the Nanodevice Fabrication and Transport Laboratory of UNIMORE (https://www.nanofab.unimore.it/) jointly with the computational research groups of CNR-NANO specialized in biophysics (https://www.nano.cnr.it/researcher-profile/andrea-mescola/), molecular dynamics and multiscale simulations (https://www.nano.cnr.it/researcher-profile/giorgia-brancolini/).

Collaborations:
Nanotechnology Group, University of Salamanca, Prof. Enrique Diez (https://lbt.usal.es/) University of Minnesota, Prof. Vlad Pribiag (https://www-users.cse.umn.edu/~vpribiag/research.php)

References and links:
[1] Impact of Mg Doping on Structural, Morphological and Thermoelectric Properties of SnO2 Nanoparticles: A Combined Experimental-Theoretical Investigation, M Isram, et al., 2026, Molecules 30, 4135
[2] Chiral induction at the nanoscale and spin selectivity in electron transmission in chiral methylated BEDT-TTF derivatives, A. Carella et al. 2025 Nanoscale 17, 2599-2607
[3] Computational Simulation of a Surface Plasmonic Resonance Biosensor for β2-Microglobulin Based on Electrolyte-Gated Graphene, G. Baridi et al., 2026 Sensors 26, 2815
[4] Unveiling Complementary Unipolar Electrical Transport in ZnO‐Co3O4 Core–Shell Nanowires Exploiting Iontronics, V. Demontis et al., Advanced Materials Technologies 11, e01453
[5] The iontronic quantum dot, D. Prete at al., Nano Lett. 2026, 26, 2, 691–698
[6] Protein–surface interactions in nano-scale biosensors for IL-6 detection using functional monolayers S Giberti, S Dutta, S Corni, M Frasconi, G Brancolini, 2025 Nanoscale 17 (8), 4389-4399

Physics Education Research (PER) addresses the teaching and learning of physics, from
secondary school to university. A core strand integrates digital technologies into active-
learning environments reforming physics laboratory courses around an inquiry-based
epistemology, and increasingly investigates the use of generative AI in physics education.
Across these directions we combine the design of innovative instruction with the empirical
study of how students learn, using both quantitative and qualitative methods. Several lines of
research are possible, according to the student’s interests.
A first line investigates whether and under what conditions generative AI can support
students’ reasoning in active, inquiry-based physics. The project will design and evaluate AI
tools that scaffold reasoning across problem solving and laboratory inquiry (e.g., Socratic
tutoring and automated, rubric-based feedback) [1, 2], and will use AI-assisted methods
such as text embeddings to analyse how students reason [3]. A complementary strand
evaluates the models’ own physics reasoning and its limits [4]. Emerging applications
include supporting students’ physics quantitative literacy.
A further line aims at integrating quantum literacy into secondary school curricula. Building
on previous research and educational activities aimed at introducing the fundamental
concepts of quantum mechanics in a rigorous yet mathematically accessible way, it will be
explored how these approaches can be integrated into school curricula, foster connections
with other disciplines, particularly chemistry, and support the development of students’
critical thinking in relation to pseudoscientific claims (in collaboration with Valentina De
Renzi)
A possible line focuses on making selected concepts in cosmology accessible and engaging
to secondary school students, connecting familiar physics with the frontiers of science, such
as dark matter. The innovative approach lies in merging real scientific data-based inquiry
with familiar curriculum-based tools, enabling students to “do” cosmology, strengthening
their confidence in quantitative reasoning and the scientific method (in collaboration with
Guido Goldoni and Enrico Bertuzzo)
References

1. Tufino, E., & Gregorcic, B. (2025). Creating a customisable Socratic AI physics tutor.
   Physics Education 60(6), 065037.
2. Tufino, E. (2025). NotebookLM as a Socratic physics tutor: Design and preliminary
   observations of a RAG-based tool. The Physics Educator
3. Tufino, E. (2025). Exploring large language models (LLMs) through interactive Python
   activities. Physics Education 60(5), 055003.
4. Tufino, E., Giovanzana, C., Zamboni, A., Onorato, P., & Oss, S. (2026). Performance
   and failure modes of AI chatbots on a novel concept inventory on relativity in classical
   mechanics. arXiv:2605.09602.