arXiv subjects

Panagiotis Grammatikopoulos

Publications and source records attributed to Panagiotis Grammatikopoulos.

5 recordsLinked to original sources

arXiv2026

Polaron Conductivity in $α$-Fe2O3 Quenched by Adsorbed NO2

Polaron-mediated charge transport in α-Fe2O3 plays a central role in its performance as a gas-sensing material, yet the atomistic interaction between surface adsorbates and polarons remains insufficiently understood. Here, density functional theory with Hubbard-U correction (DFT+U) combined with nudged elastic band calculations is used to investigate polaron formation, migration, and quenching at the Fe-terminated α-Fe2O3 (0001) surface. The calculated activation energy for small-polaron hopping in bulk α-Fe2O3 is found to be 0.12 eV, in excellent agreement with experimental measurements, confirming the validity of the computational approach. Slab calculations show that migration of the polaron from bulk to the surface lowers the energy by 0.12 eV, indicating preferential localization of charge carriers at the gas-solid interface. Adsorption of NO2 induces substantial electron transfer (0.72 e-) from the oxide to the molecule, eliminating the localized Fe2+ polaron state and thereby suppressing polaronic conductivity. These results provide a direct microscopic explanation for the resistance increase of hematite-based sensors upon exposure to oxidizing gases. More broadly, the study establishes how surface adsorption can modulate charge transport α-Fe2O3 through control of polaron populations, offering design principles for improved iron oxide gas sensors.

cond-mat.mtrl-sci↗

arXiv2026

Effect of independent parameters on nanoparticle sizes in magnetron-sputtering inert-gas condensation

Magnetron-sputtering inert-gas condensation (MS-IGC) provides a scalable, environmentally friendly vapour-phase synthesis approach for preparing customised nanoparticles (NPs) with bespoke properties via fine-tuning several deposition parameters. However, this high-level control comes with a caveat: the synthesis mechanisms are affected by deposition parameters in complicated ways, often yielding unpredictable outputs. This report details the working mechanism of a typical MS-IGC system, achieving in situ synthesis, size screening, and directional deposition of nanoclusters through the synergistic operation of the three vacuum chambers (condensation, screening, and deposition). The study systematically explores the regulation laws of multiple key process parameters (e.g., inert-gas flows, aggregation length, etc.) on the formation, size distribution, and deposition behaviour of nanoclusters, to rationalise their chosen values toward optimised output. To this end, multiple linear regression analysis was performed to isolate the effect of each deposition parameter and thus quantify its effect on the NP size and size distribution. Our results indicate that parameters that may prolong the nascent NPs' residence inside the condensation chamber (most prominently, the exit nozzle diameter) can positively affect the final NP size. This study expands the understanding of NP formation, enabling improved experimental control and process optimisation.

cond-mat.mtrl-sci↗

arXiv2026

The evolution of AI from image interpretation toward scientific inference in nanoparticle electron microscopy

Artificial intelligence (AI) is transforming electron microscopy by enabling quantitative analysis of increasingly large and complex datasets for nanoparticle characterization. Recent advances in machine learning (ML) and deep learning (DL) have expanded microscopy from a descriptive imaging technique into a data-driven platform for structural interpretation, dynamic analysis, and scientific inference. This review examines AI methodologies for nanoparticle electron microscopy, focusing on transmission electron microscopy (TEM), high-resolution transmission electron microscopy (HRTEM), scanning transmission electron microscopy (STEM), and in situ TEM. The discussion is organized around the principal challenges in nanoparticle characterization, including particle detection, segmentation, morphology quantification, atomic-resolution restoration, defect identification, two-dimensional-to-three-dimensional structural inference, and analysis of dynamic processes in situ. We review computational approaches from conventional ML and convolutional neural networks to transformer architectures, self-supervised learning, foundation models, multimodal AI, and physics-informed learning. We further discuss integrating microscopy data with simulations, metadata, and autonomous experimentation to relate nanoparticle structure, dynamics, synthesis conditions, and functional properties. The advantages, limitations, benchmarking, and data requirements of current methodologies are critically assessed. Finally, emerging opportunities for foundation models, AI-guided microscopy, closed-loop experimentation, and autonomous materials discovery are discussed. By integrating advances across computer vision, materials informatics, and electron microscopy, this review highlights the role of AI in next-generation nanoparticle characterization and accelerated materials discovery.

cond-mat.mtrl-sci↗

arXiv2026

Structure-Dependent Chemical Order Modification in Strained Alloy Nanoparticles

Alloy nanoparticles (nanoalloys) exhibit tuneable physicochemical properties that depend sensitively on their atomic arrangement, making control over chemical ordering a central challenge in nanomaterials design. While most theoretical studies consider nanoalloys in vacuum, practical systems are typically supported, where strong cluster-substrate interactions can introduce significant lattice strain. Here, we investigate strain as a control parameter for chemical ordering in bimetallic nanoalloys using atomistic molecular dynamics and Monte Carlo simulations. By imposing controlled tensile and compressive strain through an implicit anchored interface, we systematically probe the response of NiPt nanoparticles with distinct structural motifs. For truncated octahedral particles, we find that chemical ordering and segregation behaviour remain remarkably robust even under large strains, indicating that intrinsic thermodynamic preferences dominate. In contrast, icosahedral nanoparticles exhibit pronounced strain-induced chemical redistribution, with a significant increase in surface Ni concentration under tensile strain. This behaviour is attributed to the combined effects of intrinsic geometric frustration and a high fraction of undercoordinated sites in icosahedral structures. Our results demonstrate that strain can selectively modulate chemical ordering in nanoalloys in a structure-dependent manner, establishing a general framework for understanding strain-induced chemical ordering in nanoalloys.

cond-mat.mtrl-sci↗

arXiv2026

Nanoscale Phase Distribution Governs Exchange Bias in Multiphase Magnetic Nanoparticles

Exchange bias at ferromagnet-antiferromagnet interfaces underpins magnetic memory, spintronic devices, and nanoscale electromagnetic technologies, yet its behaviour in complex nanoscale heterostructures remains poorly understood. Here we uncover how exchange bias emerges in functional multiphase metal-oxide nanoparticles by combining gas-phase synthesis, advanced magnetic characterisation, and first-principles-informed spin-dynamics simulations. Using Ni-Cr/NiO nanoparticles as a model system, we show that exchange bias is governed not simply by the presence of ferromagnetic and antiferromagnetic phases, but critically by their nanoscale spatial distribution and interfacial topology. At 10 K, significant negative exchange bias (0.8 kOe) and coercivity enhancement (1.4 kOe) was exhibited; both decreased due to either Cr-segregation (at low Cr content) or to Cr accumulation inside the core (at high Cr content). The resulting competition between magnetic phases produces a temperature-driven inversion from negative to positive exchange bias and a crossover from exchange-dominated to dipolar interactions. By linking density-functional-theory calculations directly to spin-dynamics simulations of nanoparticle ensembles, we establish a predictive framework for designing exchange-coupled nanomagnets capable of operating beyond the superparamagnetic limit.

cond-mat.mtrl-sci↗