jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Adrian Braun | Henrik Dick | Timon Sieweke | Alexander Kunzmann | Klara Lünser | Gabi Schierning | Thomas Dahm
Source: ArXiv.org (article, 2026-01-21)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Influence of Charge Density Waves on the Hall coefficient in NiTi We present a mean-field charge density wave theory for NiTi using density functional theory bandstructure as a starting point. We calculate the Hall coefficient as a function of temperature and compare with recent experimental results. We analyze the contributions to the Hall coefficient from different parts of the Fermi surface and find that the Hall coefficient is dominated by certain ``hot spots''. The analysis shows that these hot spots are mostly dominated by Ni d-orbitals. We demonstrate that the Hall coefficient is not well reproduced by Boltzmann transport theory within the constant relaxation time approximation without charge density waves. We consider both uniaxial and biaxial charge density waves and show that biaxial charge density waves can account well for the Hall coefficient, while uniaxial cannot. We also investigate the temperature dependence of the resistivity and the specific heat. Hall effect ; Charge density ; Electrical resistivity and conductivity ; Charge (physics) ; Relaxation (psychology) ; Fermi surface ; Boltzmann constant ; Charge density wave Organic and Molecular Conductors Research ; Topological Materials and Phenomena ; Shape Memory Alloy Transformations
Authors: Hana Kim | Myeong-Won Bae | Yong-jin Cho | Dong-Hoon Lee
Source: Agriculture (article, 2026-01-09)
Field: Chemistry — Analytical Chemistry

Spatiotemporal Analysis for Real-Time Non-Destructive Brix Estimation in Apples Predicting internal quality parameters, such as Brix and water content, of apples, is essential for quality control. Existing near-infrared (NIR) and hyperspectral imaging (HSI)-based techniques have limited applicability due to their dependence on equipment and environmental sensitivity. In this study, a transportable quality assessment system was proposed using spatiotemporal domain analysis with long-wave infrared (LWIR)-based thermal diffusion phenomics, enabling non-destructive prediction of the internal Brix of apples during transport. After cooling, the thermal gradient of the apple surface during the cooling-to-equilibrium interval was extracted. This gradient was used as an input variable for multiple linear regression, Ridge, and Lasso models, and the prediction performance was assessed. Overall, 492 specimens of 5 cultivars of apple (Hongro, Arisoo, Sinano Gold, Stored Fuji, and Fuji) were included in the experiment. The thermal diffusion response of each specimen was imaged at a sampling frequency of 8.9 Hz using LWIR-based thermal imaging, and the temperature changes over time were compared. In cross-validation of the integrated model for all cultivars, the coefficient of determination (R2cv) was 0.80, and the RMSEcv was 0.86 °Brix, demonstrating stable prediction accuracy within ±1 °Brix. In terms of cultivar, Arisoo (Cultivar 2) and Fuji (Cultivar 5) showed high prediction reliability (R2cv = 0.74–0.77), while Hongro (Cultivar 1) and Stored Fuji (Cultivar 4) showed relatively weak correlations. This is thought to be due to differences in thermal diffusion characteristics between cultivars, depending on their tissue density and water content. The LWIR-based thermal diffusion analysis presented in this study is less sensitive to changes in reflectance and illuminance compared to conventional NIR and visible light spectrophotometry, as it enables real-time measurements during transport without requiring a separate light source. Surface heat distribution phenomics due to external heat sources serves as an index that proximally reflects changes in the internal Brix of apples. Later, this could be developed into a reliable commercial screening system to obtain extensive data accounting for diversity between cultivars and to elucidate the effects of interference using external environmental factors. Diffusion ; Hyperspectral imaging ; Thermal ; Thermography ; Sampling (signal processing) ; Brix ; Near-infrared spectroscopy Spectroscopy and Chemometric Analyses ; Greenhouse Technology and Climate Control ; Postharvest Quality and Shelf Life Management
Authors: Alejandro Flores Sepulveda | Jean-Louis Reymond
Source: ChemRxiv (article, 2026-02-09)
Field: Materials Science — Materials Chemistry

Nested TMAPs to Visualize Billions of Molecules Here we present a visualization and clustering framework enabling the exploration of billion-sized chemical datasets, exemplified with the REAL database of 9.6 billion make-on-demand molecules. We represent molecules as 42-dimensional MQN (molecular quantum numbers) fingerprints describing molecular structures with counts for different atom-and bond-types, polar groups and topological features, and cluster the dataset by applying Product Quantization (PQ) and PQkmeans. We retrieve the molecule closest to the cluster centroid as a representative for each cluster and compute a tree-map (TMAP) displaying these representatives organized by MQN-similarity. Each cluster representative in this primary TMAP is linked to a nested secondary TMAP displaying the corresponding cluster content organized by ECFP4 substructure fingerprint similarity. This nested TMAP approach can be computed on a single workstation and gives direct access to the entire dataset down to single molecular structures in two clicks. A nested TMAP for the REAL database is accessible at https://chelombus.gdb.tools/databases/real-database. Cluster (spacecraft) ; Cluster analysis ; Visualization ; Centroid ; Substructure ; Data visualization Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Molecular spectroscopy and chirality
Authors: Xiaoming Wang | Longfeng Lv | Yu Zou | Guofeng Song | Bo Cheng | Kunpeng Zhai | Hanxiao Shao
Source: Photonics (article, 2026-03-26)
Field: Materials Science — Electronic, Optical and Magnetic Materials

The Design of a Superchiral-Sensitive MCT Photodetector Based on Silicon Metasurfaces with Truncated Corners The on-chip detection of circularly polarized light is pivotal for advancing applications in quantum optics, information processing, and spectroscopic sensing. However, conventional chiral metasurfaces often suffer from complex multilayer fabrication, material incompatibility, or modest performance, hindering their integration with photonic circuits. Here, we introduce a monolithic all-silicon metasurface that overcomes these limitations through a singular structural innovation. By strategically truncating four corners of a conventional Z-shaped meta-atom, we induce a hybridization of optical modes that profoundly enhances chiral light–matter interaction. This deliberately engineered perturbation yields a colossal circular dichroism with an extinction ratio exceeding 66 dB, a performance that surpasses existing state-of-the-art designs by approximately three orders of magnitude. Furthermore, the proposed metasurface exhibits remarkable fabrication robustness, owing to its single-layer architecture and CMOS-compatible material. We demonstrate that this exceptional metasurface can be directly integrated with a Mercury Cadmium Telluride (MCT) photodetector to form a highly efficient, compact circular polarization detector. Our work provides a simple yet powerful paradigm for creating high-performance chiral photonic devices, paving the way for their widespread adoption in integrated optoelectronics. Photodetector ; Photonics ; Extinction ratio ; Fabrication ; Metamaterial ; Circular polarization ; Circular dichroism ; Silicon Metamaterials and Metasurfaces Applications ; Plasmonic and Surface Plasmon Research ; Photonic Crystals and Applications
Authors: Jingjie Niu | Jiahui Lyu | Jie Li | Koyal Suman Samantaray | Cheolhwa Jang | Y. J. Lee | Ji‐Sang Park | S. W. Lee
Source: Advanced Science (article, 2026-01-29)
Field: Materials Science — Materials Chemistry

All‐Optical Control of Bidirectional Polarization Switching in Ferroelectric Heterostructures for Neuromorphic and In‐Memory Computing All-optical in-memory computing is emerging as a critical technology for next-generation energy-efficient and high-speed information processing because it avoids frequent optical-electrical-optical conversions and integrates sensing, processing, and memory within a single device. Here, we report the demonstration of bidirectional polarization switching in a van der Waals heterostructure composed of ferroelectric CuInP<sub>2</sub>S<sub>6</sub> (CIPS) and semiconducting MoS<sub>2</sub>. Wavelength-tunable excitation (660-405 nm) enables robust, bidirectional polarization reversal through the interaction between the photogenerated charges in MoS<sub>2</sub>/CIPS heterostructure and ferroelectric polarization charges in CIPS. Two wavelength-dependent carrier dynamic mechanisms were established specifically for excitations below and above the CIPS bandgap. These mechanisms result in opposite charge accumulation at the interface, leading to opposite polarization switching directions. The device demonstrates high-performance all-optical nonvolatile memory. Furthermore, it emulates all-optical controlled retina-like synaptic plasticity, including paired-pulse facilitation/inhibition, short-term and long-term potentiation and depression, and learning-forgetting behaviours, with wavelength-selective long-term potentiation and depression enabling neuromorphic image recognition. Additionally, the single device implements reconfigurable all-optical controlled Boolean logic gates. Neuromorphic engineering ; Ferroelectricity ; Heterojunction ; Polarization (electrochemistry) ; Non-volatile memory ; Long-term potentiation ; Excitation 2D Materials and Applications ; Advanced Memory and Neural Computing ; Neural Networks and Reservoir Computing
Authors: Nguyen Duc Thanh | Nguyễn Mạnh Sơn | Nguyen Duc Phong | Pham Huu Vang | Ninh Duc Ha | Nguyen Thi Van Anh | Vu Thi Trang | Nguyen Manh Ha | Nguyen Thi Kim Thuong | Ta Thi Thao
Source: ASEAN Journal of Scientific and Technological Reports (article, 2026-01-21)
Field: Chemistry — Analytical Chemistry

Rapid Identification of Orange Juice Adulteration Using Voltammetric Profiling and Machine Learning In this study, the differential pulse voltammetry with a gold electrode and machine learning was employed to detect adulteration in orange juices. The method assessed both natural and commercial juices, along with their mixtures containing known proportions of natural juice. Initially, an unsupervised machine learning algorithm, Cluster Analysis (CA), was used to highlight differences, demonstrating the ability to distinguish between natural and flavored orange juices. Subsequently, supervised machine learning methods, including Interval Partial Least Squares – Linear Discriminant Analysis (iPLS-LDA) and Interval Partial Least Squares – Random Forest (iPLS-RF), were applied for classification purposes. The RF model achieved up to 95% classification accuracy, greatly exceeding 67.5% of iPLS- LDA. This enables reliable detection of orange juice adulteration. The RF model struggled to accurately distinguish between the “Natural” and “Mixed” categories, particularly for samples containing a medium proportion of natural orange juice (around 45–50%). The integration of voltammetric fingerprints with machine learning enabled a fast, cost-effective classification method for on-site analysis with portable sensors. This approach proved more efficient than other complex analytical techniques. Orange juice ; Partial least squares regression ; Linear discriminant analysis ; Orange (colour) ; Pattern recognition (psychology) ; Support vector machine Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Edible Oils Quality and Analysis
Authors: Rajan Gowsalya | Monirul Shaikh | Sathiyamoorthy Buvaneswaran | Saurabh Ghosh
Source: ArXiv.org (article, 2026-03-16)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Polar Charge-Ordered States in BiFeO$_3$/CaFeO$_3$ Superlattice Oxide superlattices represent a potent avenue for tailoring emergent electronic phases through sophisticated interfacial charge transfer and dynamic lattice distortions. This study systematically investigates the structural and electronic attributes of the BiFeO$_3$/CaFeO$_3$ superlattice, leveraging a comprehensive approach that integrates first-principles computations with detailed symmetry-mode analysis. The strategic integration of polar bismuth ferrite alongside charge-transfer calcium ferrite instigates profound lattice instabilities, notably manifest in octahedral rotations and cooperative FeO$_6$ breathing modes that might not necessarily be soft. However, their synergistic coupling stabilizes a non-centrosymmetric $Pc$ ground state that intrinsically features polar charge ordering of Fe ions. This resultant phase ingeniously unifies C-type antiferromagnetism with robust ferroelectric semiconductor characteristics, exhibiting a calculated indirect band gap of about 0.6 eV. Our discoveries firmly establish ferrite superlattices as an exceptionally versatile and tunable platform for the rational design of next-generation multifunctional materials, offering precise control over polarization, charge ordering phenomena, and electronic transport behavior via advanced interface and strain engineering techniques. Superlattice ; Ferroelectricity ; Electronic structure ; Band gap ; Antiferromagnetism ; Lattice (music) ; Semiconductor ; Charge ordering Multiferroics and related materials ; Magnetic Properties and Synthesis of Ferrites ; Heusler alloys: electronic and magnetic properties
Authors: S. N. PRAJAPATI
Source: Research Square (preprint, 2026-03-25)
Field: Materials Science — Materials Chemistry

The Role of Quasiperiodicity on the Electronic Structure of Elements Quasiperiodicity ; Quasiperiodic function ; Pseudogap ; Quasicrystal ; Icosahedral symmetry ; Electronic structure ; Phase diagram ; Fermi surface Quasicrystal Structures and Properties ; Microstructure and mechanical properties ; Intermetallics and Advanced Alloy Properties
Authors: Olivia Gampp | Riccardo Cadalbert | Roland Riek | Sarah A. Overall
Source: Journal of Biomolecular NMR (article, 2026-03-31)
Field: Chemistry — Spectroscopy

Super-Resolution solid-state NMR Spectroscopy Solid-state NMR spectroscopy is often limited by low spectral resolution, a problem typically addressed using fast magic-angle spinning (MAS) and ¹H detection, which require costly specialized hardware. Here, we demonstrate that the super-resolution method-previously applied in solution-state NMR-can be successfully implemented in solid-state NMR to enhance resolution. Applying dynamic number of scans (DNS) sampling to 2D ¹³C-¹³C DARR experiments on the AP205 capsid protein yielded an effective doubling of resolution, halving peak widths from ~ 180 Hz to ~ 87 Hz. Furthermore, DNS acquisition provides a significant advantage over post-acquisition apodization of conventional data with a 20% gain in sensitivity, yielding 309 more detectable peaks with 20% more sequential contacts and 25% more long-range contacts. This method is simple to implement and provides a powerful, accessible strategy to greatly improve the quality of solid-state NMR spectra applicable at all MAS frequencies. Nuclear magnetic resonance spectroscopy ; Apodization ; Two-dimensional nuclear magnetic resonance spectroscopy ; Spectroscopy ; Spinning ; Analytical Chemistry (journal) ; Spectral line ; Sampling (signal processing) Advanced NMR Techniques and Applications ; NMR spectroscopy and applications ; Protein Structure and Dynamics
Authors: XU Wen-ji | Kai Song | Lianzheng Sun | Haolin Lu | Hengyuan Zhang | Yiran Feng
Source: Preprints.org (preprint, 2026-01-01)
Field: Chemistry — Analytical Chemistry

A Deep Learning–Driven Method for Bowl Tableware Reconstruction and the Prediction of Liquid Volume and Food Nutrient Content To overcome the low accuracy of conventional methods for estimating liquid volume and food nutrient content in bowl-type tableware, as well as the tool dependence and time-consuming nature of manual measurements, this study proposes an integrated approach that combines geometric reconstruction with deep learning–based segmentation. After a one-time camera cali-bration, only a frontal and a top-down image of a bowl are required. The pipeline automatically extracts key geometric information, including rim diameter, base diameter, bowl height, and the inner-wall profile, to complete geometric modeling and capacity computation. The estimated parameters are stored in a reusable bowl database, enabling repeated predictions of liquid vol-ume and food nutrient content at different fill heights. We further propose Bowl Thick Net to predict bowl wall thickness with millimeter-level accuracy. In addition, we developed a Geome-try-aware Feature Pyramid Network (GFPN) module and integrated it into an improved Mask R-CNN framework to enable precise segmentation of bowl contours. By integrating the contour mask with the predicted bowl wall thickness, precise geometric parameters for capacity estima-tion can be obtained. Liquid volume is then predicted using the geometric relationship of the liq-uid or food surface, while food nutrient content is estimated by coupling predicted food weight with a nutritional composition database. Experiments demonstrate an arithmetic mean error of −3.03% for bowl capacity estimation, a mean liquid-volume prediction error of 9.24%, and a mean nutrient-content (by weight) prediction error of 11.49% across eight food categories. Volume (thermodynamics) ; Pipeline (software) ; Pyramid (geometry) ; Content (measure theory) ; Approximation error ; Feature (linguistics) ; Mean squared error ; Nutrient Spectroscopy and Chemometric Analyses ; Food Supply Chain Traceability ; Edible Oils Quality and Analysis
Authors: Felix Mütter | Pavle Boškoski | Hartmuth Schröttner | Christoph Hochenauer | Vanja Subotić
Source: Journal of Power Sources (article, 2026-02-19)
Field: Materials Science — Materials Chemistry

Bi-hourly EIS-DRT diagnostics over 2650 h benchmark degradation of a commercial SOEC under steam electrolysis, co-electrolysis and reversible operation Long-term durability and operating-mode flexibility are barriers to commercial solid oxide electrolysis (SOE) for hydrogen production and supplying syngas for synthetic-fuel synthesis. We report a high-resolution degradation benchmark of a commercial 4 × 4 cm 2 electrolyte-supported cell operated for 2650 h under six modes that cover steam electrolysis, co-electrolysis at Fischer–Tropsch-relevant syngas conditions at 300 and 500 mA cm − 2 , repeated steam electrolysis, and reversible electrolysis and fuel cell cycling at 800 °C and fixed gas flows. Bi-hourly electrochemical impedance spectroscopy (EIS) is analysed using a Deep-Prior Distribution of Relaxation Times (DP-DRT)-derived neural DRT solver that processes more than 1200 spectra within minutes, enabling time-resolved separation of ohmic and polarisation contributions. Performance loss is dominated by an increase in ohmic resistance R s , while polarisation resistance shows only modest net changes. Co-electrolysis mainly adds a low-frequency polarisation contribution that is largely reversible when returning to steam electrolysis. In late-life steam electrolysis at high steam fraction, accelerated ohmic ageing emerges after substantial cumulative operation. Post-mortem SEM reveals fuel-side microstructural coarsening and larger voids. The combination of a single-cell, single-sequence multi-mode campaign with bi-hourly EIS and consistent DRT-based batch inversion provides a uniquely time-resolved, internally comparable degradation dataset that goes beyond end-point voltage trends and enables mode-specific attribution of ageing contributions. • 2650 h single-cell benchmark across steam, co-electrolysis and EC/FC cycling. • Bi-hourly EIS with neural DP-DRT inversion enables rapid analysis of 1200+ spectra. • Degradation dominated by rising ohmic resistance R s , with modest net R p changes. • Co-electrolysis adds low-frequency polarisation that is largely reversible in steam. • Post-mortem SEM shows fuel-side coarsening and larger voids consistent with R s rise. Polymer electrolyte membrane electrolysis ; High-temperature electrolysis ; Ohmic contact ; Hydrogen production ; Electrolysis ; Hydrogen ; Syngas ; Degradation (telecommunications) Advancements in Solid Oxide Fuel Cells ; CO2 Reduction Techniques and Catalysts ; Catalysts for Methane Reforming
Authors: Gergely Kőrösy | András Roósz | Ádám Végh | Tamás Mende
Source: Metals (article, 2026-01-11)
Field: Materials Science — Materials Chemistry

The ESTPHAD Concept: An Optimised Set of Simplified Equations to Estimate the Equilibrium Liquidus and Solidus Temperatures, Partition Ratios and Liquidus Slopes for Quick Access to Equilibrium Data in Solidification Software Part III: Ternary Eutectic-Type Equilibrium Phase Diagram The liquidus and solidus temperatures, the initial temperature of the solidification of binary eutectics, and the partition ratios of the solid solution at the Al corner of the ternary eutectic-type Al-Si-Cu alloy system were calculated using the thermodynamically based ESTPHAD method. It is shown that these data can be calculated from the liquidus and solidus data of the two binary equilibrium phase diagrams (first estimation), the binary phase diagram and the eutectic valleys in the ternary system (second estimation), as well as the binary phase diagram, the eutectic valleys, and one (third estimation) and more (fourth estimation) liquidus and solidus temperatures of the ternary equilibrium phase diagram with varying precisions. A database calculated with Thermo-Calc software (version 4.1.0.4995), was used for the calculations. Liquidus ; Solidus ; Eutectic system ; Phase diagram ; Binary number ; Ternary numeral system ; Ternary operation ; CALPHAD Solidification and crystal growth phenomena ; Aluminum Alloy Microstructure Properties ; Metallurgical Processes and Thermodynamics
Authors: Alejandro Flores Sepulveda | Jean-Louis Reymond
Source: ChemRxiv (article, 2026-03-23)
Field: Materials Science — Materials Chemistry

Nested TMAPs to Visualize Billions of Molecules Here we present a visualization and clustering framework enabling the exploration of billion-sized chemical datasets, exemplified with the REAL database of 9.6 billion make-on-demand molecules. We represent molecules as 42-dimensional MQN (molecular quantum numbers) fingerprints describing molecular structures with counts for different atom-and bond-types, polar groups and topological features, and cluster the dataset by applying Product Quantization (PQ) and PQkmeans. We retrieve the molecule closest to the cluster centroid as a representative for each cluster and compute a tree-map (TMAP) displaying these representatives organized by MQN-similarity. Each cluster representative in this primary TMAP is linked to a nested secondary TMAP displaying the corresponding cluster content organized by ECFP4 substructure fingerprint similarity. This nested TMAP approach can be computed on a single workstation and gives direct access to the entire dataset down to single molecular structures in two clicks. A nested TMAP for the REAL database is accessible at https://chelombus.gdb.tools/databases/real-database. Cluster (spacecraft) ; Cluster analysis ; Visualization ; Centroid ; Substructure ; Data visualization Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Molecular spectroscopy and chirality