Materials Science
Direct Raman observation of the quantum metric in a quantum magnet
Chao-Fan Wang, Han Ge, Jun-Yang Chen et al. (+4 more)
The
quantum geometric tensor (QGT) unifies the Berry
curvature (its imaginary part) and the
quantum metric (its real part), yet Raman studies of
chiral phonons have so far accessed only the former. We perform circularly polarized Raman spectroscopy on the
quantum magnet K2Co(SeO3)2, where the field-odd
chiral splitting and the field-even center-frequency shift collapse onto a single curve across temperature and
magnetic field, revealing a common microscopic origin for both observables. Since the
chiral splitting reflects the Berry
curvature, the concomitant even component, arising from the same microscopic origin, captures the field-induced change of the
quantum metric, corresponding to the diagonal Born-Oppenheimer correction. Across two resolvable Eg modes, the unified data are well captured by a simple empirical quadratic relation. These results establish Raman spectroscopy as a direct probe of the
quantum metric and an operational decomposition of
quantum geometry within a single measurement.
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All authors Chao-Fan Wang, Han Ge, Jun-Yang Chen, Liusuo Wu, Xiaobin Chen, Jia-Wei Mei, Mingyuan Huang
Materials Science
Dual-Mode Luminescent Thermometry in LiYO2:Nd3+,Yb3+ Enabled by Structural Phase Transition and Phonon-Assisted Energy Transfer
M. Tahir Abbas, M. Szymczak, D. Szymanski et al. (+3 more)
In this work, a dual-mode luminescent thermometer operating via both ratiometric and lifetime-based readout strategies was developed, enabled by the coexistence of two thermally driven effects: a structural phase transition in LiYO2 and a
phonon-assisted energy transfer from Yb3+ to Nd3+. As demonstrated, changes in the shape of the emission band of Yb3+ ions corresponding to the 2F5/2 -> 2F7/2 electronic transition, induced by the phase transition, enabled the design of a ratiometric thermometer with a maximum relative sensitivity (SR) of 3.1% K^-1 for LiYO2 doped with 10% Yb3+ and 1% Nd3+ at 290 K. In contrast, the temperature-dependent Yb3+ -> Nd3+ energy transfer facilitated the development of a lifetime-based thermometer with a maximum SR of 1.5% K^-1 for 20% Nd3+ at 378 K. In both approaches, tuning the Nd3+ concentration allowed modulation of both the sensitivity and the temperature at which the maximum SR occurred. This was achieved by shifting the phase transition temperature and increasing the probability of interionic energy transfer, respectively. Notably, the temperature ranges corresponding to the maximum SR for the ratiometric and lifetime modes were distinct, effectively broadening the thermal operating window of the sensor. Additionally, it was shown that LiYO2 doped with Nd3+ and Yb3+ can also be used as a temperature sensor through the ratio of luminescence intensities recorded at two different time gates. Furthermore, the results confirmed that the
phonon-assisted energy transfer process plays the dominant role in shaping the luminescence kinetics, surpassing the influence of the structural phase transition. Overall, this study identifies LiYO2:Nd3+,Yb3+ as a promising candidate for
multimodal luminescent temperature sensing applications.
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All authors M. Tahir Abbas, M. Szymczak, D. Szymanski, M. Drozd, G. Chen, L. Marciniak
Materials Science
Scalable data-driven modeling of microstructure evolution by learning local dependency and spatiotemporal translation invariance rules in phase field simulation
Zishuo Lan, Qionghuan Zeng, Weilong Ma et al. (+10 more)
Phase-field (PF) simulation provides a powerful framework for predicting microstructural
evolution but suffers from prohibitive computational costs that severely limit accessible spatiotemporal scales in practical applications. While data-driven methods have emerged as promising approaches for accelerating PF simulations, existing methods require extensive training data from numerous
evolution trajectories, and their inherent black-box nature raises concerns about long-term prediction reliability. This work demonstrates, through examples of grain growth and spinodal decomposition, that a minimalist Convolutional Neural Network (CNN) trained with a remarkably small
dataset even from a single small-scale simulation can achieve seamless scalability to larger systems and reliable long-term predictions far beyond the temporal range of the training data. The key insight of this work lies in revealing that the success of CNN-based models stems from the
alignment between their inductive biases and the physical priors of phase-field simulations specifically, locality and spatiotemporal translation invariance. Through effective receptive field analysis, we verify that the model captures these essential properties during training. Therefore, from a reductionist perspective, the surrogate model essentially establishes a spatiotemporally invariant regression mapping between a grid point's local environment and its subsequent state. Further analysis of the model's feature space demonstrates that microstructural
evolution effectively represents a continuous redistribution of a finite set of local environments. When the model has already encountered nearly all possible local environments in the early-stage training data, it can reliably generalize to much longer
evolution timescales, regardless of the dramatic changes in global microstructural morphology.
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All authors Zishuo Lan, Qionghuan Zeng, Weilong Ma, Xiangju Liang, Yue Li, Yu Chen, Yiming Chen, Xiaobing Hu, Junjie Li, Lei Wang, Jing Zhang, Zhijun Wang, Jincheng Wang
Materials Science
Wavelength-commensurate anatase TiO\_2 particles for ro-bust and functional Mie resonances across the visible and near infrared
Pedro Tartaj, Yurena Luengo, Pedro Moronta et al. (+3 more)
Earth-abundant materials exhibiting Mie resonances across the visible and near-infrared offer opportunities for efficient and sustainable sensing, thermal regulation, and sunlight harvesting. For anatase TiO$_2$, a broadband optical and abundant material, Mie calculations indicate that robust resonances require size tunability and monodispersity (standard deviation, $\lesssim$ 5%) over an extended range (0.5-2 $\mu$m) not yet experimentally covered, while maintaining a refractive index above 2 to ensure optical contrast, for example with biomolecules. Here, we demonstrate that a simple UV-assisted thermal hydrolysis route yields anatase particles that meet all these criteria and remain aqueous-processable. Consequently, the materials display intense and modulable Mie resonances across the visible/near-infrared regions (including biological windows), outperforming previous results that were limited by size. Strong optical resonances combined with ambient-temperature processability enable robust, broadband, label-free detection of transparent biomolecules. Our insights advance precise synthesis and Mie-based photonics of an earth-abundant material, and indicate the potential for cost-effective and rapid-on-demand integration via printing technologies, further enhanced by anatase's biocompatibility and photochemical properties.
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All authors Pedro Tartaj, Yurena Luengo, Pedro Moronta, Luisina Forzani, Alvaro Blanco, Cefe López
Materials Science
Magnetic Frustration Enforced Electronic Reconstruction in Ni intercalated NbSe$_{2}$: Suppression of Electronic Orders
Ashutosh S. Wadge, Alexander Kazakov, Xujia Gong et al. (+14 more)
We investigate the single
crystals of Ni$_{0.19}$NbSe$_2$, revealing that Ni intercalation profoundly alters the physical properties of NbSe$_2$.
Magnetic measurements clearly show that the system is magnetically frustrated with
antiferromagnetic ordering below 23.5\,K, with an irreversibility temperature near 10\,K, and a
magnetic hysteresis with a small net
magnetic moment. Overall, the system can be described as an inhomogeneous
antiferromagnetic phase with
magnetic disorder and
magnetic frustration. We found two Curie-Weiss temperatures of -80\,K for the field in the ab-plane and -137\,K for the field out of plane, which are a consequence of anisotropic interactions in
spin space and favor an orientation of the
spin along the c-axis. Temperature-dependent resistivity shows a complete suppression of both charge density waves and superconducting order down to 300\,mK. Angle-resolved photoemission spectroscopy at 84\,K reveals a $\overline{\Gamma}$-centered electron pocket in Ni$_{0.19}$NbSe$_2$, which is absent in pristine NbSe$_2$. The electronic structure results show a shift of the van Hove singularity (VHS), which is the main cause of the suppression of the electronic orders. These results align with recent theoretical predictions that Ni intercalation with cationic disorder favors frustrated
antiferromagnetic stripe states, shifts the VHS and reconstructs the
Fermi surface in NbSe$_2$. Our findings position Ni$_{0.19}$NbSe$_2$ within a magnetically frustrated, non-superconducting regime, highlighting how partial intercalation and disorder drive complex
magnetic order and the
Fermi surface reconstruction in low-dimensional
quantum materials.
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All authors Ashutosh S. Wadge, Alexander Kazakov, Xujia Gong, Daniel Jastrzebski, Bogdan J. Kowalski, Artem Lynnyk, Lukasz Plucinski, Amar Fakhredine, Ryszard Diduszko, Marta Aleszkiewicz, Jedrzej Korczak, Dawid Wutke, Marcin Rosmus, Rafal Kurleto, Natalia Olszowska, Carmine Autieri, Andrzej Wisniewski
Materials Science · Cross-listed from physics.med-ph
Lanthanides-Based Nanoparticles Conjugated with Rose Bengal for FRET-Mediated X-Ray-Induced PDT
Batoul Dhaini, Joël Daouk, Hervé Schohn et al. (+8 more)
In order to find a good candidate for Förster Resonance Energy Transfer (FRET)-mediated X-ray-induced photodynamic therapy (X-PDT) for the treatment of cancer, lanthanide (Ln)-based AGuIX nanoparticles (NPs) conjugated with Rose Bengal (RB) as a photosensitizer (PS) were synthesized. X-PDT overcomes the problem of the poor penetration of visible light into tissues, which limits the efficacy of PDT in the treatment of deep-seated tumors. It is essential to optimize FRET efficiency by maximizing the overlap integral between donor emission and acceptor absorption and lengthening the duration of the donor emission. In this study, we optimized energy transfer between a scintillator (Sc) as a donor and a PS as an acceptor. Terbium (Tb) and Gadolinium (Gd) as Scs and Rose RB as a PS were chosen. The study of energy transfer between Tb, Gd and RB in solution and chelated on AGuIX NPs proved to be FRET-like. RB was conjugated directly onto AGuIX NPs (i.e., AGuIX Ln@RB), and the use of a spacer arm (i.e., AGuIX Ln@spacer arm-RB) increased FRET efficiency. Singlet oxygen production by these NPs was observed under UV–visible illumination and X-ray irradiation. The in vitro bioassay demonstrated 52%
cell death of U-251MG derived from human malignant glioblastoma multiforme at a concentration of 1 $\mu$M RB after illumination and irradiation (2 Gy, 320 kV, 10 mA, 3 Gy/min at 47 cm). In addition, the RB-coupled NRP-1-targeting peptide (i.e., K(RB)DKPPR) was conjugated onto AGuIX NPs by a thiol-maleimide click chemistry reaction, and an affinity in the nM range was observed.
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All authors Batoul Dhaini, Joël Daouk, Hervé Schohn, Philippe Arnoux, Valérie Jouan-Hureaux, Albert Moussaron, Agnès Hagege, Mathilde Achard, Samir Acherar, Tayssir Hamieh, Céline Frochot
Materials Science
MATAI: A Generalist Machine Learning Framework for Property Prediction and Inverse Design of Advanced Alloys
Yanchen Deng, Chendong Zhao, Yixuan Li et al. (+10 more)
The discovery of advanced metallic
alloys is hindered by vast composition spaces, competing property objectives, and real-world constraints on manufacturability. Here we introduce MATAI, a generalist machine learning framework for property prediction and inverse design of as-cast
alloys. MATAI integrates a curated
alloy database, deep neural network-based property predictors, a constraint-aware optimization engine, and an iterative AI-experiment feedback loop. The framework estimates key mechanical propertie, sincluding density, yield strength, ultimate tensile strength, and elongation, directly from composition, using multi-task learning and physics-informed inductive biases.
Alloy design is framed as a constrained optimization problem and solved using a bi-level approach that combines local search with symbolic constraint programming. We demonstrate MATAI's capabilities on the Ti-based
alloy system, a canonical class of lightweight structural materials, where it rapidly identifies candidates that simultaneously achieve lower density (<4.45 g/cm3), higher strength (>1000 MPa) and appreciable ductility (>5%) through only seven iterations. Experimental validation confirms that MATAI-designed
alloys outperform commercial references such as TC4, highlighting the framework's potential to accelerate the discovery of lightweight, high-performance materials under real-world design constraints.
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All authors Yanchen Deng, Chendong Zhao, Yixuan Li, Bijun Tang, Xinrun Wang, Zhonghan Zhang, Yuhao Lu, Penghui Yang, Jianguo Huang, Yushan Xiao, Cuntai Guan, Zheng Liu, Bo An
Materials Science
pynxtools: A Python framework for generating and validating NeXus files in experimental data workflows
Sherjeel Shabih, Lukas Pielsticker, Florian Dobener et al. (+19 more)
Scientific data across physics, materials science, and materials engineering often lacks adherence to FAIR principles (Barker et al., 2022; Jacobsen et al., 2020; M. D. Wilkinson et al., 2016; S. R. Wilkinson et al., 2025) due to incompatible instrument-specific formats and diverse standardization practices. pynxtools is a Python software development framework with a command line interface (CLI) that standardizes data conversion for scientific experiments in materials science to the NeXus format (Klosowski et al., 1997; Könnecke, 2006; Könnecke et al., 2015) across diverse scientific domains. NeXus defines data storage specifications for different experimental techniques through application definitions. pynxtools provides a fixed, versioned set of NeXus application definitions that ensures convergence and
alignment in data specifications across, among others, atom probe
tomography, electron microscopy, optical spectroscopy, photoemission spectroscopy, scanning probe microscopy, and X-ray diffraction. Through its modular plugin architecture pynxtools provides conversion of data and metadata from instruments and electronic lab notebooks to these unified definitions, while performing validation to ensure data correctness and NeXus compliance. pynxtools can be integrated directly into Research Data Management Systems (RDMS) to facilitate parsing and normalization. We detail one example for the RDM system NOMAD. By simplifying the adoption of NeXus, the framework enables true data interoperability and FAIR data management across multiple experimental techniques.
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All authors Sherjeel Shabih, Lukas Pielsticker, Florian Dobener, Andrea Albino, Theodore Chang, Carola Emminger, Lev Ginzburg, Ron Hildebrandt, Markus Kühbach, Rubel Mozumder, Tommaso Pincelli, Martin Aeschlimann, Marius Grundman, Walid Hetaba, Carlos-Andres Palma, Laurenz Rettig, Markus Scheidgen, José Antonio Márquez Prieto, Claudia Draxl, Sandor Brockhauser, Christoph Koch, Heiko B. Weber
Materials Science
Graphene assisted III-V epitaxy towards substrate recycling
Naomie Messudom, Antonella Cavanna, Ali Madouri et al. (+6 more)
Re-using the substrate is identified as a method for reducing the cost of high efficiency III-V
solar cells. The approach investigated here consists in inserting a
graphene layer onto a (001)GaAs substrate prior to the epitaxial growth of GaAs. To obtain a monocrystalline GaAs grown layer, the
graphene layer is patterned, followed by a two-step epitaxial growth, here performed by molecular beam epitaxy (MBE). The first step is a selective area growth of GaAs in
graphene openings, followed by a lateral overgrowth, under a modulated Ga flux. The second step, after reaching coalescence, consists in a regular growth under continuous Ga supply. It is observed that the pattern orientations relative to the crystallographic direction of the GaAs substrate below the
graphene have an influence on GaAs morphology and quality. The best result was obtained for patterns oriented along [1__10]+22.5{\textdegree} with a
graphene coverage of 50%, with a significantly reduced roughness down to 3.3 nm.
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All authors Naomie Messudom, Antonella Cavanna, Ali Madouri, Carlos Macias, Nathalie Bardou, Laurent Travers, Stéphane Collin, Jean-Christophe Harmand, Amaury Delamarre
Materials Science
Giant tunneling magnetoresistance based on spin-valley-mismatched ferromagnetic metals
Kan Yan, Li Cheng, Yizhi Hu et al. (+3 more)
Half metals, which are amenable to perfect
spin filtering, can be utilized for high-magnetoresistive devices. However, available half metals are very limited. Here, we demonstrate that materials with intrinsic
spin-valley-mismatched (SVM) states can be used to block charge transport, resembling half metals and leading to
giant tunneling magnetoresistance. As an example, by using first-principles transport calculations, we show that
ferromagnetic 1T-VSe$_2$, 1T-VS$_2$, and 2H-VS$_2$ are such
spin-valley-mismatched metals, and
giant magnetoresistance of more than 99% can be realized in
spin-valve van der Waals (vdW) junctions using these metals as electrodes. Owing to the intrinsic mismatch of
spin states, the central-layer materials for the vdW junctions can be arbitrary nonmagnetic materials, in principle. Our research provides clear physical insights into the mechanism for high magnetoresistance and opens new avenues for the search and design of high-magnetoresistance devices.
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All authors Kan Yan, Li Cheng, Yizhi Hu, Junjie Gao, Xiaolong Zou, Xiaobin Chen
Materials Science
Numerical Modeling of Cu2MnSnS4/FeSi2 Dual-Absorber Solar Cell Achieving High Efficiency
Hasib Md Abid Bin Farid and Md Tashfiq Bin Kashem
Dual-absorber
solar cells represent a promising approach to surpass the efficiency limit of single-junction devices by extending spectral absorption and minimizing thermalization losses. Among earth-abundant thin-film materials, kesterites have attracted considerable interest, however, the well-studied Cu2ZnSnS4 (CZTS) continues to face challenges related to antisite disorder and secondary phase formation. Replacing Zn with Mn in Cu2MnSnS4 (CMTS) mitigates these limitations, improving cation ordering and electronic quality while maintaining favorable optical properties. Yet, despite its potential, CMTS remains largely unexplored in multi-absorber configurations-only one prior study has reported a CMTS-based dual-absorber device. In this work, we present a comprehensive numerical investigation of a CMTS-FeSi2 dual-absorber thin-film
solar cell using the one-dimensional
solar cell capacitance simulator (SCAPS-1D). FeSi2, with its narrow band gap (0.87 eV) and strong near-infrared absorption, serves as an ideal bottom absorber to complement CMTS, enabling broader spectral utilization. The study systematically examines the effects of geometrical, electronic, and interfacial parameters on carrier transport, and overall device performance. The optimized device delivers an impressive power conversion efficiency of 34.9%, with VOC = 0.79 V, JSC = 51.07 mA/cm2, and a fill factor of 85.91%. These findings demonstrate that integrating FeSi2 with CMTS not only enhances carrier collection and spectral harvesting but also establishes a new pathway toward high-efficiency, sustainable, and environmentally benign thin-film photovoltaics.
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Materials Science
Electric Field-Induced Phase Transitions and Hysteresis in Ferroelectric HfO2 Captured with Machine Learning Potential
Po-Yen Chen and Teruyasu Mizoguchi
Electric field-induced studies, including phase transition and polarization hysteresis, for ferroelectric HfO2 at the atomic scale are critical since they can largely affect its application in ferroelectric and dielectric devices. However, conventional first-principles approaches are computationally limited in capturing large-scale atomic dynamics under realistic field conditions. Here, to enable electric-field-driven molecular dynamics simulations, we develop a machine learning potential (MLP) tailored for HfO2, coupled with an in-situ Born effective charge (BEC) model. This framework enables us to capture key phenomena, including field-induced phase transitions, polarization switching, and strain-dependent dielectric responses, with high fidelity and computational efficiency. Notably, we reproduce hysteresis loops and phase transition barriers consistent with AIMD results and reveal possible electric-field-induced polarization activation in the monoclinic phase. Our approach offers a scalable and transferable tool for atomistic exploration of functional
oxides and paves the way for data-driven design of ferroelectric devices.
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Materials Science · Cross-listed from physics.optics
Broadband Solar Selective Absorber with Dallenbach-type Bilayer Structure Achieved Using Carbon Nanotube Membranes with Tailored Optical Spectra
Hengkai Wu, Taishi Nishihara, Mioko Hizukuri et al. (+3 more)
Spectrally selective absorbers that maximize
solar absorption and minimize thermal radiation loss are crucial for efficient
solar thermal energy harvesting. However, limitations imposed by the intrinsic properties of conventional materials hinder the fabrication of interference-based absorbers with desired optical properties. Herein, a high-performance
solar absorber with a Dallenbach-type dielectric-metal tandem structure was fabricated using an ultrathin, subquarter-wavelength thickness single-walled carbon nanotube (SWCNT) membrane with tailored optical spectra as the absorbing layer. By mixing multiple SWCNT
chiral structures, the optical response of the absorbing layer was tailored to approximate a theoretical complex refractive index spectrum of dielectrics; therefore enabling high
solar absorptance, low infrared emittance, and a low angular dependence with a simple
bilayer structure. The fabricated proof-of-concept 1 cm$^2$ absorber exhibited excellent spectral selectivity (
solar absorptance/infrared emittance of 0.84/0.03), and an
equilibrium temperature of $\approx 190^\circ$C ($270^\circ$C) under nonconcentrated ($2\times$ concentrated) sunlight, considerably outperformed a blackbody-like absorber. This study presents a high-performance
solar absorber with a simple planar structure and proposes a concept of designing dielectric with the desired optical properties by mixing various types of structure-sorted SWCNTs.
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All authors Hengkai Wu, Taishi Nishihara, Mioko Hizukuri, Takeshi Tanaka, Hiromichi Kataura, Yuhei Miyauchi
Materials Science
Re-refinement of the structure of the planar hexagonal phase of ZnO nanocrystals
Musen Li, Lingyao Zhang, Wei Ren et al. (+1 more)
The planar hexagonal phase of ZnO, known as h-ZnO, g-ZnO, {\alpha}-ZnO, the Bk structure, the 5-5 phase, the {\alpha}-BN phase, etc., has P63/mmc
symmetry and is implicated in ferroelectric switching mechanisms for wurtzite-ZnO. It is well-known in thin films on substrates and to be stabilized by external pressure, but critical is its possible existence in high-purity nanocrystals under ambient conditions. Indeed, a
crystal structure has been reported, but this work remains controversial as first-principles calculations predict very different structural properties. Herein, the original experimental data is re-refined, through phase-shift determination and Morlet wavelet transformation, that molecular dynamics simulations associate with a P63/mmc structure with
lattice parameters at room temperature of a = 3.45{\pm}0.02 Å and c = 4.46{\pm}0.02 Å. These values are 0.35 Å and 0.80 Å, respectively, larger than those previously reported and in good agreement with computational predictions. This confirms that ZnO nanocrystals can form a metastable planar hexagonal phase. It provides key information pertaining to polarization switching in ZnO, its derivatives, and general wurtzite-structured materials.
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All authors Musen Li, Lingyao Zhang, Wei Ren, Jeffrey R. Reimers
Materials Science
Beyond empirical models: Discovering new constitutive laws in solids with graph-based equation discovery
Hao Xu, Yuntian Chen and Dongxiao Zhang
Constitutive models are fundamental to solid mechanics and materials science, underpinning the quantitative description and prediction of material responses under diverse loading conditions. Traditional phenomenological models, which are derived through empirical fitting, often lack generalizability and rely heavily on expert intuition and predefined functional forms. In this work, we propose a graph-based equation discovery framework for the automated discovery of constitutive laws directly from multisource experimental data. This framework expresses equations as directed graphs, where nodes represent operators and variables, edges denote computational relations, and edge features encode parametric dependencies. This enables the generation and optimization of free-form symbolic expressions with undetermined material-specific parameters. Through the proposed framework, we have discovered new constitutive models for strain-rate effects in
alloy steel materials and the deformation behavior of lithium metal. Compared with conventional empirical models, these new models exhibit compact analytical structures and achieve higher accuracy. The proposed graph-based equation discovery framework provides a generalizable and interpretable approach for data-driven scientific modelling, particularly in contexts where traditional empirical formulations are inadequate for representing complex physical phenomena.
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Materials Science
Potential-Programmed Operando Ensembles Govern Nitrate Electroreduction
Xue-Chun Jiang, Jia-Lan Chen, Wei-Xue Li et al. (+1 more)
Electrocatalyst surfaces continuously reorganize on the timescale of catalytic turnover, obscuring the identification of active sites under operando conditions and hindering rational catalyst design. Here, we resolve the operando Cu(111) electrolyte interface for nitrate-to-ammonia electroreduction (NO3RR) via a multiscale modeling framework accelerated by a coverage-aware machine-learning potential. Rather than a single "average coverage" site, the working interface is a potential-gated statistical ensemble of 34 interconverting adsorbate motifs between -0.10 and -1.00 V (vs. SHE). Potential-driven shifts in motif populations produce a volcano-shaped activity trend peaking at -0.70 V, where the site-normalized turnover frequency reaches 0.015 s-1 with nearly 100% Faradaic efficiency to ammonia. The activation barriers across >150 transition states collapse into a single linear relationship with the excess charge on the reacting Cu atoms ({\Delta}qCu), identifying interfacial charge redistribution as a unifying kinetic descriptor. The maximum activity arises not from uniform moderate coverage but from a 2NO/2NH2 quadrilateral microensemble that tunes {\Delta}qCu to an intermediate value, simultaneously lowering the N-O cleavage and N-H formation barriers. Reconceptualizing "coverage" as an ensemble of local microenvironments decouples thermodynamic stability from catalytic productivity. This perspective furnishes a parameter-free strategy by controlling motif populations and interfacial charge via the potential to program high-coverage electrocatalysis beyond the NO3RR.
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All authors Xue-Chun Jiang, Jia-Lan Chen, Wei-Xue Li, Jin-Xun Liu
Materials Science
Multibit Ferroelectric Memcapacitor for Non-volatile Analogue Memory and Reconfigurable Filtering
Deepika Yadav, Spyros Stathopoulos, Patrick Foster et al. (+4 more)
Tuneable capacitors are vital for adaptive and reconfigurable electronics, yet existing approaches require continuous bias or mechanical actuation. Here we demonstrate a voltage-programmable ferroelectric memcapacitor based on HfZrO that achieves more than eight stable, reprogrammable capacitance states (3-bit encoding) within a non-volatile window of 24~pF. The device switches at low voltages (3~V), with each state exhibiting long retention (10^5~s) and high endurance (10^6 cycles), ensuring reliable multi-level operation. At the nanoscale, multistate charge retention was directly visualised using atomic force microscopy, confirming the robustness of individual states beyond macroscopic measurements. As a proof of concept, the capacitor was integrated into a high-pass filter, where the programmed capacitive states shift the cutoff frequency over 5~kHz, establishing
circuit-level viability. This work demonstrates the feasibility of CMOS-compatible, non-volatile, analogue
memory based on ferroelectric HfZrO, paving the way for adaptive RF filters, reconfigurable analogue front-ends, and neuromorphic electronics.
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All authors Deepika Yadav, Spyros Stathopoulos, Patrick Foster, Andreas Tsiamis, Mohamed Awadein, Hannah Levene, Themis Prodromakis