jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Glenn Francis | Suryapratap Ray | S. Jeyakumar | Srihari Supreethee | Rahul Vashishth
Source: Food Research International (review, 2025-11-28)
Field: Materials Science — Materials Chemistry

Hybrid sonics for multi-pollutant removal from food matrices: A review Sustainability ; Process (computing) ; Hybrid system ; Sonication ; Food safety ; Sizing ; Quality (philosophy) ; Food industry Ultrasound and Cavitation Phenomena ; Microbial Inactivation Methods ; Bacterial biofilms and quorum sensing
Authors: Marvin Alberts (21727874) | Nina Hartrampf (5491757) | Teodoro Laino (1986436)
Source: Figshare (article, 2025-07-16)
Field: Materials Science — Materials Chemistry

From Spectrato Structure: AI-Powered <sup>31</sup>P NMR Interpretation Phosphorus-31 nuclear magnetic resonance (<sup>31</sup>P NMR) spectroscopy is a powerful technique for characterizing phosphorus-containing compounds in diverse chemical environments. However, spectral interpretation remains a time-consuming and expertise-dependent task, relying on reference tables and empirical comparisons. In this study, we introduce a data-driven approach that automates <sup>31</sup>P NMR spectral analysis, providing rapid and accurate predictions of the local phosphorus environments. By leveraging a curated data set of experimental and synthetic spectra, our model achieves a Top-1 accuracy of 53.64% and a Top-5 accuracy of 77.69% at predicting the local environment around a phosphorus atom. Furthermore, it demonstrates robustness across different solvent conditions and outperforms expert chemists by 25% in spectral assignment tasks. The models, data sets, and architecture are openly available, facilitating seamless adoption in chemical laboratories engaged in structure elucidation, with the goal of advancing <sup>31</sup>P NMR spectral analysis and interpretation. Robustness (evolution) ; Spectral line ; Interpretation (philosophy) ; Nuclear magnetic resonance spectroscopy ; NMR spectra database ; Set (abstract data type) ; Spectroscopy ; Experimental data Machine Learning in Materials Science ; Organophosphorus compounds synthesis ; Phosphorus compounds and reactions
Authors: Tarcis Teles Xavier Da Silva | Sheila Catarina de Oliveira | Paulo Henrique Gonçalves Dias Diniz | Noemi Nagata
Source: Food Chemistry (article, 2025-09-03)
Field: Chemistry — Analytical Chemistry

Assessment of yerba mate quality based on branch content via digital image analysis Food science ; Digital image analysis ; Content (measure theory) ; Digital content ; Quality (philosophy) ; Chemistry ; Computer science ; Computer vision ; Mathematics ; World Wide Web ; Physics Heavy Metals in Plants ; Radioactivity and Radon Measurements ; Spectroscopy and Chemometric Analyses
Authors: Junxin Chen | Shengyu Cong | Riping Liu | Jiayao Duan | Chaoyue Chen | Dongsheng Yu | Xiuyuan Zhu | Chong Ran | Du Cheng | Zhengke Li | Iain McCulloch | Wan Yue
Source: Angewandte Chemie (article, 2025-01-29)
Field: Materials Science — Polymers and Plastics

Imine‐Based Polymeric Mixed Ionic–Electronic Conductors Featuring Degradability and Biocompatibility for Transient Bioinspired Electronics Abstract Degradable features are highly desirable to advance next‐generation organic mixed ionic–electronic conductors (OMIECs) for transient bioinspired artificial intelligence devices. It is highly challenging that OMIECs exhibit excellent mixed ionic‐electronic behavior and show degradability simultaneously. Specially, in OMIECs, doping is often a tradeoff between structural disorder and charge carrier mobilities. Here, we describe a regiochemistry‐driven backbone curvature approach to prepare OMIECs, enabling doped state ordered within efficient ionic‐electronic conduction in organic electrochemical transistors (OECTs) and presenting degradable characteristics. Significantly, i ‐3gTIT shows an outstanding mobility (1.99 cm 2 V −1 s −1 ) and μ C* (302 F V −1 cm −1 s −1 ), and presents higher disorder‐tolerance upon doping and faster degradation behavior than its regioisomer, o ‐3gTIT . Especially, the resulting OECT‐based inverter shows a high voltage gain of 31.6 V V −1 at a low driving voltage of 0.6 V. Moreover, we demonstrate an application of transient OECT, i. e., biodegradable solid‐state electrolyte of OECT‐based artificial synapses. Remarkably, the regiochemistry‐driven film crystallinity modulation enables the conversion from volatile to non‐volatile operation in such synapses. The transient synapse based on i ‐3gTIT achieves over 90 % recognition accuracy for small digit handwritten images, showing potential in security neuromorphic computing. Our work is the first presentation enabling excellent mixed conduction of OMIECs with degradable features for transient bioinspired electronics. Biocompatibility ; Ionic bonding ; Electronics ; Electrical conductor ; Nanotechnology ; Imine ; Transient (computer programming) ; Chemistry ; Materials science ; Organic chemistry ; Ion ; Physical chemistry ; Catalysis Conducting polymers and applications ; Advanced Sensor and Energy Harvesting Materials ; Supercapacitor Materials and Fabrication
Authors: Zhou Xuan-Chi | Ji Jia-hui | Xiaohui Yao
Source: Acta Physica Sinica (article, 2025-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Hydrogen ion regulation of magnetoelectric transport properties in correlated oxide heterojunctions Hydrogenation or protonation provides a feasible pathway for exploring exotic physical functionality and phenomena within correlated oxide system through introducing an ion degree of freedom. This breakthrough provides great potential for enhancing the application of multidisciplinary equipment in the fields of artificial intelligence, related electronics and energy conversions. Unlike traditional substitutional chemical doping, hydrogenation enables the controllable and reversible control over the charge-lattice-spin-orbital coupling and magnetoelectric states in correlated system, without being constrained by the solid-solution limits. Our findings identify proton evolution as a powerful tuning knob to cooperatively regulate the magnetoelectric transport properties in correlated oxide heterojunction, specifically in metastable VO&lt;sub&gt;2&lt;/sub&gt;(B)/La&lt;sub&gt;0.7&lt;/sub&gt;Sr&lt;sub&gt;0.3&lt;/sub&gt;MnO&lt;sub&gt;3&lt;/sub&gt;(LSMO) systems grown via laser molecular beam epitaxy (LMBE). Upon hydrogenation, correlated VO&lt;sub&gt;2&lt;/sub&gt;(B)/LSMO heterojuction undergoes a reversible magnetoelectric phase transition from a ferromagnetic half-metallic state to a weakly ferromagnetic insulating state. This transition is accompanied by a pronounced out-of-plane lattice expansion due to the incorporation of protons and the formation of O—H bonds, as confirmed by X-ray diffraction (XRD). Proton evolution extensively suppresses both the electrical conductivity and ferromagnetic order in the pristine VO&lt;sub&gt;2&lt;/sub&gt;(B)/LSMO system. Remarkably, these properties recover through dehydrogenation via annealing in an oxygen-rich atmosphere, underscoring the high reversibility of hydrogen-induced magnetoelectric transitions. Spectroscopic analyses, including X-ray photoelectron spectroscopy (XPS) and synchrotron-based soft X-ray absorption spectroscopy (sXAS), provide further insights into the physical origin underlying the hydrogen-mediated magnetoelectric transitions. Hydrogen-related band filling in the &lt;i&gt;d&lt;/i&gt;-orbital of correlated oxides accounts for the electron localization in VO&lt;sub&gt;2&lt;/sub&gt;(B)/LSMO heterostructure through hydrogenation, while the suppression of the Mn&lt;sup&gt;3+&lt;/sup&gt;-Mn&lt;sup&gt;4+&lt;/sup&gt; double exchange leads to the magnetic transitions. This work not only expands the hydrogen-related phase diagram for correlated oxide system but also establishes a versatile pathway for designing exotic magnetoelectric functionalities via ionic evolution, which has great potential for developing protonic devices. Ferromagnetism ; Oxide ; Heterojunction ; Phase transition ; X-ray photoelectron spectroscopy ; Spectroscopy ; Metastability ; Ion Multiferroics and related materials ; Magnetic and transport properties of perovskites and related materials ; Heusler alloys: electronic and magnetic properties
Authors: Jonas Elsborg | Luca Thiede | Alán Aspuru‐Guzik | Tejs Vegge | Arghya Bhowmik
Source: ArXiv.org (preprint, 2025-03-11)
Field: Materials Science — Materials Chemistry

ELECTRA: A Cartesian Network for 3D Charge Density Prediction with Floating Orbitals We present the Electronic Tensor Reconstruction Algorithm (ELECTRA) - an equivariant model for predicting electronic charge densities using floating orbitals. Floating orbitals are a long-standing concept in the quantum chemistry community that promises more compact and accurate representations by placing orbitals freely in space, as opposed to centering all orbitals at the position of atoms. Finding the ideal placement of these orbitals requires extensive domain knowledge, though, which thus far has prevented widespread adoption. We solve this in a data-driven manner by training a Cartesian tensor network to predict the orbital positions along with orbital coefficients. This is made possible through a symmetry-breaking mechanism that is used to learn position displacements with lower symmetry than the input molecule while preserving the rotation equivariance of the charge density itself. Inspired by recent successes of Gaussian Splatting in representing densities in space, we are using Gaussian orbitals and predicting their weights and covariance matrices. Our method achieves a state-of-the-art balance between computational efficiency and predictive accuracy on established benchmarks. Furthermore, ELECTRA is able to lower the compute time required to arrive at converged DFT solutions - initializing calculations using our predicted densities yields an average 50.72 % reduction in self-consistent field (SCF) iterations on unseen molecules. Atomic orbital ; Tensor (intrinsic definition) ; Position (finance) ; Charge (physics) ; Gaussian ; STO-nG basis sets ; Charge density ; Slater-type orbital ; Molecular orbital Machine Learning in Materials Science ; Quantum many-body systems ; Advanced Chemical Physics Studies
Authors: Selene van der Poel | Geertje ter Maat | Oliver Plümper | Richard Wessels
Source:  (preprint, 2025-03-14)
Field: Materials Science — Materials Chemistry

The EXCITE&amp;#178; Network The EXCITE&amp;#178; Network is transforming Earth and environmental material science with transnational access to 36 worldclass European imaging facilities in 19 research institutes across 12 European and partner countries. Researchers anywhere can now explore complex processes in Earth materials across scales ranging from nanometers to decimeters. This yields unprecedented insights into critical areas such as environmental toxicity and human health, sustainable extraction of critical metals for renewable energy, and safe long-term storage of climate-relevant gases.&amp;#160;&amp;#160; EXCITE&amp;#178; also brings together expertise and pioneers innovative services, tools, and training, to enhance the ability of users to address complex scientific challenges. To this end, EXCITE&amp;#178;&amp;#160;will launch the &amp;#8216;EXCITE Academy&amp;#8217; in the Spring of 2025, an open community and collaborative platform for sharing knowledge, tools, experiences and expertise via live and online events as well as an open online searchable database: the &amp;#8216;Academy Hub&amp;#8217;. Innovative services and tools include AI-driven data analysis and next-generation imaging technologies. &amp;#160; By fostering interdisciplinary collaboration between academia, industry, and diverse scientific fields, EXCITE&amp;#178; accelerates innovation and strengthens Europe's position in global sustainability efforts. The initiative actively supports capacity building through tailored training programs for early-career researchers, fully embedded within the principles of European open science.&amp;#160;&amp;#160; Through its commitment to scientific excellence, sustainability, and societal impact, EXCITE&amp;#178; is shaping the future of Earth and environmental research. Interested in joining the network? Apply for transnational access via our open call! Visit the EXCITE&amp;#178;&amp;#160;website (https://excite-network.eu) for more information. Chemistry ; Physics Machine Learning in Materials Science ; Research Data Management Practices ; Technology Assessment and Management
Authors: Zhenhao Xia | Peipei Xu | Baijie Cheng | Xiaoyong tianxiaoyong | Shanyi Guang | Hongyao Xu
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Materials Chemistry

Design of T/H-Mos₂/Cu₃Mo₂O₉ Intelligent Antibacterial Nano-Platform with Z-Scheme Heterojunction Structure Heterojunction ; Nano- ; Scheme (mathematics) ; Materials science ; Nanotechnology ; Optoelectronics ; Composite material ; Mathematics ; Mathematical analysis Nanocluster Synthesis and Applications ; Metal-Organic Frameworks: Synthesis and Applications ; Supramolecular Self-Assembly in Materials
Authors: Pallavi Rao Netrakanti | Yukti Chopra | Rahul Agarwal | Premchand Rajeev | Shekhar Kedia | Mini Jose | Suhita Nadkarni | Deepak Nair
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2025-03-15)
Field: Chemistry — Electrochemistry

Nanoscale Lattice Organization of Molecular Condensates Drives Compositional Degeneracy in Synaptic Plasticity Abstract Synaptic plasticity is essential for neuronal communication, involving coordinated structural, molecular and functional changes that are shaped by the nanoscale alterations of the active zone and postsynaptic density. Emerging evidence suggests that synapses function as complex information processing machines, where unique molecular assemblies shape transmission properties. Central to this is the organization of voltage-gated calcium channels (VGCCs) and Bassoon within active zones. Utilizing advanced techniques like liquid-liquid phase separation, super-resolution microscopy, and data-driven models of synaptic transmission, we reveal how nanoscale "compositional degeneracy" in Bassoon and VGCCs enables synapses to achieve functional adaptability through multiple molecular configurations. By modulation of local uncertainty and implementing probabilistic inference, synapses fine-tune transmission efficiency by regulating dynamic entropy and free energy. These principles are especially evident during homeostatic scaling, where synaptic scaling mechanisms differ with neuronal maturity. This study highlights how distinct thermodynamic states in VGCC and Bassoon organization optimize information transfer at different plasticity stages. Our findings propose a refined framework for understanding synaptic transmission as an adaptable, entropy-modulated process, balancing resilience and efficiency. Synaptic plasticity ; Neuroscience ; Neurotransmission ; Synaptic scaling ; Metaplasticity ; Nanotechnology ; Computer science ; Materials science ; Biology Electrochemical Analysis and Applications ; Neural dynamics and brain function ; Advanced Memory and Neural Computing
Authors: Faith O. Osabuohien
Source:  (article, 2025-11-05)
Field: Chemistry — Spectroscopy

Chemo-Metric Optimization for Robust and Green Chromatographic Methods Developing chromatographic methods usually encounters challenges of achieving analytical robustness and adhering to the principles of greenness through Green Analytical Chemistry (GAC). Traditional One-Variable-at-a-Time (OVAT) optimization techniques often fail to capture complex factor interactions, resulting in inefficient methods and subsequent environmental impacts. This paper introduced chemometrics, which encompasses multivariate analysis (MVA), Design of Experiments (DoE), and Monte Carlo simulations as a data-driven and systematic solution. Chemometric optimization helps to identify Critical Method Parameters (CMPs) and facilitates the development of Method Operable Design Region (MODR), within which analytical performance is reliable and consistent. The integration of GAC metrics, such as the Analytical GREEnness (AGREE) calculator, would promote sustainability through chemometric approaches by reducing solvent consumption, lowering energy demands, minimizing waste generation, and shorter analysis times. Thus, the relationship between chemometrics and GAC provides a framework for developing efficient, robust, and environmentally responsible chromatographic methods that comply with regulatory expectations and Quality-by-Design (QbD) principles. Chemometrics ; Robustness (evolution) ; Monte Carlo method ; Design of experiments ; Multivariate statistics ; Quality by Design Analytical Chemistry and Chromatography ; Chemistry and Chemical Engineering ; Spectroscopy and Chemometric Analyses
Authors: Qingtai Xiao | Chunmei Liu | Min Wang | Wuliang Yin | Jianxin Pan | Bo Yu | Kai Yang | Hua Wang
Source: Alexandria Engineering Journal (article, 2025-10-10)
Field: Materials Science — Materials Chemistry

Standard molar entropy estimation of binary composite oxides: A novel hybrid approach based on deep learning and hyperparameters optimization The standard molar entropy (SME), a typical physical and chemical property of binary composite oxides (BCOs), is of great significance in materials science. Estimating the SME is however challenging, being unstable and difficult to precisely determine both experimentally and theoretically due to the complex internal structure and diverse interactions within BCOs. In this work, the data-driven approach was used to estimate the standard molar entropy of BCOs. Specifically, the crested porcupine optimizer (CPO) was introduced to fine-tune the hyperparameters of the bidirectional time convolutional network (BiTCN) and bidirectional gated recurrent unit (BiGRU). Results indicate that the performance of CPO-BiTCN-BiGRU-SME improves with increasing the training dataset proportion. When the training dataset proportion reaches 95 %, the root-mean-square error (RMSE), mean-absolute-percentage error (MAPE) and coefficient of determination (R 2 ) between the estimated and the experimental SME values are 15.49 J∙mol −1 ∙K −1 , 11.31 % and 0.89, respectively. Compared to CPO-BiTCN and long short-term memory (LSTM), CPO-BiTCN-BiGRU-SME achieves RMSE reductions of 10.19 J∙mol −1 ∙K −1 and 16.55 J∙mol −1 ∙K −1 , respectively, while the MAPE is reduced by 4.34 % and 8.31 %, respectively. Notably, CPO-BiTCN-BiGRU-SME outperforms various existing hybrid and single models. The superiority lies in the fact that CPO enables the novel hybrid approach to better adapt to SME data and enhance its generalization ability, BiTCN extracts local features and captures short-term dependencies efficiently, BiGRU handles long-term dependencies and integrates nature effectively. • Pioneered merging DL and hyperparameter optimization for estimating SME of BCOs. • Introduced CPO to optimize hyperparameters of BiTCN-BiGRU-SME for the first time. • Performance of CPO-BiTCN-BiGRU-SME rises notably with more training dataset of SME. • Compared with traditional methods, CPO-BiTCN-BiGRU-SME reduces RMSE and MAPE. • Offered a novel hybrid approach for facilitating materials discovery and design. Hyperparameter ; Binary number ; Pattern recognition (psychology) ; Mean squared error ; Entropy (arrow of time) ; Binary classification ; Convolutional neural network ; Cross entropy Machine Learning in Materials Science ; Thermography and Photoacoustic Techniques ; X-ray Diffraction in Crystallography
Authors: Wei Chen | Dingding Wang | Ping Xiang | Peng Shi | Junsong Hu
Source: Advances in Engineering Software (article, 2025-12-18)
Field: Materials Science — Materials Chemistry

Isogeometric free vibration analysis of size-dependent functionally graded graphene origami-enabled auxetic metamaterial microplates supported by Winkler/Pasternak/Kerr foundation Metamaterial ; Vibration ; Isotropy ; Auxetics ; Isogeometric analysis ; Stiffness ; Bending ; Convergence (economics) ; Finite element method Nonlocal and gradient elasticity in micro/nano structures ; Composite Structure Analysis and Optimization ; Advanced Materials and Mechanics
Authors: Fábio de Oliveira Neves | Mateus Cury | Ernandes Benedito Pereira | Sandra Regina Monteiro Masalskiene Roveda
Source: The Science of The Total Environment (article, 2025-10-04)
Field: Materials Science — Materials Chemistry

Waste-to-energy conversion through P-Fuzzy simulation of methane production Biogas ; Bioenergy ; Fuzzy logic ; Process (computing) ; Sustainability ; Production (economics) ; Methane ; Municipal solid waste ; Greenhouse gas ; Landfill gas Graphite, nuclear technology, radiation studies
Authors: Luca Gritti | Denny Coffetti | Marina Cabrini | Tommaso Pastore
Source: Data in Brief (article, 2025-07-21)
Field: Materials Science — Metals and Alloys

Data-driven optimisation of variation of residual concentration to estimate the hydrogen diffusion coefficient and uptake via MATLAB This article presents the data collected during experimental solubility tests in University of Bergamo lab and the developed code to estimate the principal parameters for hydrogen uptake. The experimental approach proposed involves in two-step process: the first phase consists of electrochemical charging to saturate the metallic sample via cathodic polarization at potentials lower than the equilibrium potential for hydrogen evolution, followed by a second phase of discharging under anodic polarization at potentials higher than the equilibrium potential. During the discharging phase, the time-dependent anodic current is influenced by the flux of diffusible hydrogen exiting the sample. This flux is governed by the initial concentration of diffusible hydrogen in the material, the diffusion coefficient, and the time elapsed between the end of the charging step and the beginning of the discharging phase. Via a data-driven optimisation it is possible to obtain the characteristic parameter of hydrogen diffusion in the material (apparent diffusion coefficient, hydrogen uptake and waiting time) using the MATLAB code. The data permit to elaborate the cylindrical geometry, however it is possible to modify the target curve via a simulation on a specific geometry (not included in the data) and use the same method to elaborate the experimental data of a specific geometry. MATLAB ; Diffusion ; Residual ; Coefficient of variation ; Variation (astronomy) ; Research article ; Computer science ; Statistics ; Biological system ; Thermodynamics ; Mathematics ; Algorithm ; Physics ; Biology Hydrogen embrittlement and corrosion behaviors in metals ; Nuclear Materials and Properties ; Corrosion Behavior and Inhibition