jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: A. M. Romero | Josh J. M. Kirsopp | Giuseppe Buonaiuto | Michał Krompiec
Source: arXiv (Cornell University) (preprint, 2025-09-29)
Field: Chemistry — Analytical Chemistry

Study of nuclear magnetic resonance spectra with the multi-modal multi-level quantum complex exponential least squares algorithm We present a novel application of the multi-modal, multi-level quantum complex exponential least squares (MM-QCELS) algorithm, a state-of-the-art, early fault-tolerant quantum phase estimation (QPE) technique, to the simulation and analysis of nuclear magnetic resonance (NMR) of spin systems. By leveraging the robustness and precision of MM-QCELS, we demonstrate enhanced phase resolution in quantum simulations of spin dynamics, also in systems with complex coupling topologies. Our approach enables accurate extraction of spectral features with up to an order of magnitude fewer evaluations of the time series signal in comparison with the conventional Fourier transform, making a significant step toward scalable quantum simulations of NMR Hamiltonians. This work bridges an advanced quantum algorithm design with a practical spectroscopic application, offering a promising new approach for a quantum-based chemical analysis. Robustness (evolution) ; Quantum ; Quantum algorithm ; Exponential function ; Quantum phase estimation algorithm ; Fourier transform ; Spectral line ; Quantum Fourier transform ; Coupling (piping) ; Free induction decay Spectroscopy and Chemometric Analyses
Authors: Nur Athirah Syafiqah Noramli | Noor Aida Syakira Ahmad Sabri | Muhammad Ikhsan Roslan | Nurlaila Ismail | Zakiah Mohd Yusoff | Mohd Nasir Taib
Source: International Journal of Advances in Intelligent Informatics (article, 2025-11-30)
Field: Chemistry — Organic Chemistry

Chemometric classification and authentication of four Aquilaria species from essential oil profiles using GC-MS/GC-FID and ANN Agarwood, derived from the Aquilaria species, is among the most valuable aromatic resources, yet frequent species misidentification hampers conservation efforts and compliance with trade regulations. This study applied a chemometric ANN framework to classify four Aquilaria species (A. malaccensis, A. beccariana, A. subintegra, and A. crassna) based on essential oil composition. A total of 720 samples (180 per species, each analyzed in triplicate) were extracted by hydrodistillation and profiled using GC–MS coupled to GC–FID. Six compounds were consistently detected, and three (δ-guaiene, 10-epi-γ-eudesmol, γ-eudesmol) were retained for classification based on ≥95% detection frequency and &gt;0.2% relative abundance. Pearson correlation guided feature selection, and ANN models were trained using both a 70:15:15 train–validation–test split and stratified 5-fold cross-validation with 1000 bootstrap resamples. The optimized network achieved near-perfect performance, with a mean accuracy of ~99.8% (95% CI: 99.6–100.0), and precision, recall, and F1 scores all exceeding 99.5%. In comparison, bootstrapped confidence intervals were tightly bounded at 100%, confirming robustness against data leakage. These findings demonstrate that correlation-guided feature selection combined with ANN modeling enables reproducible and highly accurate species authentication, offering a practical framework for integration into agarwood quality control, conservation monitoring, and international trade compliance. Agarwood ; Robustness (evolution) ; Pattern recognition (psychology) ; Feature selection ; Essential oil ; Feature extraction Wood and Agarwood Research ; Ginger and Zingiberaceae research ; Biological and pharmacological studies of plants
Authors: Puthalapattu Sukanya
Source:  (article, 2025-11-14)
Field: Materials Science — Materials Chemistry

Thermal Transport in Engineered Nanostructures: Disruptions in Phonon Dynamics Due to Interface Modifications The manipulation of thermal transport properties in nanoscale materials is crucial for optimizing heat dissipation in advanced electronic and energy applications. This study investigates the impact of interfacial modifications on phonon transport in p Phonon ; Dissipation ; Nanoscopic scale ; Thermal ; Interface (matter) ; Thermal conductivity ; Dynamics (music) ; Thermal management of electronic devices and systems Thermal properties of materials ; Thermography and Photoacoustic Techniques ; Advanced Thermoelectric Materials and Devices
Authors: Reva Luman
Source: International Journal of Advanced Research in Computer Science (article, 2025-10-20)
Field: Materials Science — Materials Chemistry

AI-POWERED FEEDSTOCK OPTIMIZATION IN SUSTAINABLE AVIATION FUEL PRODUCTION: ENHANCING EFFICIENCY AND REDUCING COSTS This research investigates the integration of advanced artificial intelligence (AI) techniques for optimizing feedstock combinations in Sustainable Aviation Fuel (SAF) production. The study employs a multi-layered model incorporating machine learning (ML), deep learning (DL), and reinforcement learning (RL) to enhance fuel conversion efficiency, reduce greenhouse gas (GHG) emissions, and lower production costs. Initial data collection from diverse sources, including AWS Data Exchange, Elsevier's Data API, and NASA AIRS, informs the model's development. Key machine learning algorithms such as Random Forest and Support Vector Machines (SVM) are utilized to predict energy yield and emissions, while CNNs and RNNs analyze complex relationships and time-series data for feedstock availability. The RL component dynamically optimizes feedstock blends in real-time, significantly improving operational efficiency. The model demonstrates an increase in energy output by 15-20%, a reduction in production costs by 15-25%, and a decrease in GHG emissions by 10-15%. The findings highlight the potential of AI-driven approaches to transform SAF production, ensuring both economic viability and environmental sustainability. This research contributes to the growing body of knowledge on sustainable energy solutions and offers a scalable framework for future biofuel optimization efforts. Raw material ; Aviation ; Raw data ; Scalability ; Greenhouse gas ; Production (economics) ; Benchmark (surveying) ; Efficient energy use Graphite, nuclear technology, radiation studies ; Heat transfer and supercritical fluids ; Nuclear reactor physics and engineering
Authors: Wallace Cordeiro de Morais | Gustavo Henrique Oliveira Costa | Vinícius Leal Pitcella | João Victor Vanolli Protti | Carolina Paula de Souza Moreira | José Eduardo Gonçalves | Susana Johann | Renata Barbosa de Oliveira
Source: Journal of Fungi (article, 2025-01-16)
Field: Chemistry — Organic Chemistry

Novel 2-Thiazolylhydrazone with Druggable Properties for Antifungal Application Fungal infections have become a growing concern in healthcare, particularly in immunocompromised individuals, with species like <i>Candida</i>, <i>Cryptococcus</i>, and <i>Sporothrix</i> posing significant challenges due to rising resistance and limited treatment options. In response, novel antifungal agents are being explored, including thiazolyl hydrazones. This study focuses on the development of a novel thiazolylhydrazone derivative, RW3. RW3 was synthesized to improve its water solubility and pharmacokinetic properties. The compound demonstrated a broad antifungal spectrum, particularly effective against <i>Cryptococcus neoformans</i> and <i>Candida auris</i>, with minimal irritant potential and low cytotoxicity. RW3 showed favorable solubility and high intestinal permeability, indicating potential for oral administration. The results suggest RW3 as a promising lead for further development as a therapeutic agent for systemic fungal infections. These findings underscore the importance of optimizing drug properties to enhance efficacy and safety profiles, opening the path for the development of innovative antifungal treatments. Cryptococcus neoformans ; Antifungal ; Druggability ; Cryptococcus ; Biology ; Cryptococcus gattii ; Fluconazole ; Microbiology ; Pharmacology Synthesis and biological activity ; Synthesis and Biological Evaluation ; Fungal Plant Pathogen Control
Authors: Gaoqiang Lv | Jiaqi Li | Didi Shan | Fei Liu | Mao Hanping | Weihong Sun
Source: Agronomy (article, 2025-08-30)
Field: Chemistry — Analytical Chemistry

Early Detection of Wheat Fusarium Head Blight During the Incubation Period Using FTIR-PAS The apparent normalcy of wheat during the incubation period of Fusarium head blight (FHB) makes early diagnosis challenging. This study employed Fourier transform infrared photoacoustic spectroscopy (FTIR-PAS) to conduct layer-by-layer scanning of wheat leaves during the disease outbreak stage and performed a differential spectral analysis. Spectral information was collected from five sites (D0~D4) on diseased leaves at reducing distances from the lesion caused by the Fusarium graminearum pathogen. The results revealed that the disease caused an increase in spectral similarity between deeper and shallower layers. The spectra of leaves, after removing the D0 background, showed a correlation of 83.5% to that of the pathogen, and the similarity increased at sites closer to the lesion, suggesting that the original spectra captured a large amount of hidden information related to the pathogen. With the threshold for the absorption intensity ratio of R1650/1050 for background-subtracted spectra set at 0.5, the optimal overall accuracy and F1-score were 86.0% and 0.89 for diagnosing outbreak-stage samples, respectively, while for incubation-period samples, they were 82.5% and 0.83. These results elucidate the mechanism of using FTIR-PAS to diagnose FHB during its incubation period, providing a theoretical and technical foundation for detecting disease information in other crops. Incubation period ; Fusarium ; Incubation ; Period (music) ; Horticulture ; Fungi imperfecti ; Biology ; Chemistry ; Botany ; Biochemistry Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Identification and Quantification in Food
Authors: Hongwu Zhang | Huafeng Wei | Heng Yue | Miao Yu
Source: Polymers (article, 2025-12-31)
Field: Materials Science — Polymers and Plastics

A Phosphorus–Nitrogen Synergistic Flame Retardant for Enhanced Fire Safety of Polybutadiene Polybutadiene has excellent mechanical properties and flexibility. It is widely used in elastomers and industrial fields. However, it has the characteristic of high flammability. The low LOI and rapid heat release upon ignition pose significant fire hazards. This results in a significant fire safety risk during service. Therefore, its application in some key fields has been restricted. In this study, polybutadiene with high-performance flame-retardant properties was developed by adding phosphorus-nitrogen synergistic flame retardants to address this challenge. This flame retardant mainly enhances its flame retardancy through the synergistic gas-phase and condensed-phase mechanisms. Dense and continuous carbon layers could be promoted by flame retardants during combustion. It provides an effective thermal barrier and oxygen barrier. In addition, phosphorus-containing volatiles can function by suppressing flame propagation via radical quenching in the gas phase. The modified polybutadiene reached UL-94 V-1 grade at the optimal load of 1.0 wt%. Meanwhile, its LOI increased to 27%. The cone calorimeter test further confirms a high reduction in peak heat release rate (pHRR). This work provides a feasible strategy for developing advanced polybutadiene materials. It can effectively enhance its fire safety. At the same time, it maintains a balance between flame retardancy and the overall material performance. Polybutadiene ; Fire retardant ; Cone calorimeter ; Ignition system ; Combustion ; Flame spread ; Elastomer ; Hydroxyl-terminated polybutadiene Flame retardant materials and properties ; Fire dynamics and safety research ; Synthesis and properties of polymers
Authors: Tingjun Wang | Xingan Jiang | Yida Yang | Yu Lei | Yuanyuan Cui | Chunyang Li | Guangcheng Wang | Jing Zhao | Xiaolei Wang | Xueyun Wang | Yingzhuo Lun | Jiawang Hong
Source: Advanced Functional Materials (article, 2025-11-26)
Field: Materials Science — Materials Chemistry

Giant Flexoelectricity in Silicon via Light‐Sensitive Schottky Junction Abstract Silicon is the cornerstone material of modern electronics, and has industrialized techniques for achieving large‐scale integration of functional devices. However, the lack of inversion symmetry breaking naturally determines its non‐piezoelectricity, hindering the development of silicon‐based electromechanics. Flexoelectricity induced by strain gradient is a promising way to endow silicon with electromechanical properties, but it is generally too weak to be functionalized in practical applications. Herein, a strategy is proposed to enhance the flexoelectricity of silicon by six orders of magnitude through the combination of a Schottky junction and light modulation. This flexoelectric‐like effect arises from the bending‐induced antisymmetric modulation on the Schottky barrier and the increase in carrier concentration generating by illumination. Under light illumination, the gold‐coated n ‐type silicon yields a flexoelectric coefficient with six orders of magnitude larger than the intrinsic one, reaching a maximum of 1 mC m −1 . A mechanical energy harvester proto‐device is further demonstrates the practicality of flexoelectrically modified silicon. The output normalized power density of the flexoelectric energy harvester is up to 2.5 µW cm 3 g 2 Hz −1 , being comparable to the performance of lead‐free piezoelectric ones. This work exploits the flexoelectricity in silicon, opening possibilities for high‐performance electromechanical applications based on silicon. Flexoelectricity ; Silicon ; Piezoelectricity ; Schottky diode ; Schottky barrier ; Point reflection Nonlocal and gradient elasticity in micro/nano structures ; Innovative Energy Harvesting Technologies ; Advanced Sensor and Energy Harvesting Materials
Authors: William Margerit | Nathalie Tarrat | Juan Cortés | Cathy Maugis-Rabusseau
Source: The Journal of Chemical Physics (article, 2025-06-03)
Field: Materials Science — Materials Chemistry

Structural comparison of homomolecular systems on surfaces using a fingerprint-based method This work presents an adaptation of the Smooth Overlap of Atomic Positions (SOAP) method to improve the efficiency of (dis)similarity quantification in homogeneous molecular (homomolecular) systems. SOAP, a fingerprint-based approach, is widely used to measure molecular similarity. We propose variants of SOAP kernels that leverage the structural architecture of homomolecular systems to minimize irrelevant comparisons of atomic environments. To evaluate its performance, we apply this adapted SOAP-based method to a synthetic dataset consisting of two identical tripeptides deposited on a copper surface, simulating different molecular states. The results demonstrate that the adapted method not only improves computational efficiency but also yields more meaningful clustering outcomes by better capturing the key structural differences between states. These findings suggest that the proposed method is well-suited for the study of homomolecular systems, particularly those involving surface interactions, and has the potential to enhance the use of diverse types of molecular modeling and analysis methods that rely on (dis)similarity measures. Leverage (statistics) ; Computer science ; Fingerprint (computing) ; Biological system ; Similarity (geometry) ; Molecular dynamics ; SOAP ; Cluster analysis ; Data mining ; Artificial intelligence ; Chemistry ; Computational chemistry ; Biology Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Muhammad Aqeel | Syed Aun Muhammad | Muhammad Anas | Hussain Mahmood Sargana | Muhammad Naeem | Muhammad Razi | Umair Siddique
Source: Pakistan Journal of Scientific Research (article, 2025-09-04)
Field: Chemistry — Analytical Chemistry

A Comprehensive Review: Hyperspectral and Modern Spectroscopy Identification of Yogurt Adulteration Yogurt is a valued dairy product known for its taste and texture, but these qualities also make it prone to adulteration. Common practices include adding starch, milk powders, vegetable oils, sweeteners, preservatives, and, in some cases, even harmful heavy metals, which can reduce quality and harm consumer health. Conventional analysis can be selective but is often destructive, labor-intensive, and slow for routine screening. The rapid, non-destructive alternatives provided by modern image-based spectroscopy can probe both composition and spatial heterogeneity in yogurt. This review summarizes the developments of hyperspectral imaging (HSI) and new spectroscopic methods of detecting yogurt adulterants, including Near-Infrared (NIR), Terahertz spectroscopy, Raman, Laser-Induced Breakdown Spectroscopy (LIBS), Nuclear Magnetic Resonance (NMR), vibrational mid-IR/FTIR, and UV-V are prominent methods. Spectroscopy gives robust chemical fingerprints (NIR/Raman/FTIR), and HSI gives space (LIBS/THz), as well as elemental and bulk properties; the combination of the two with ML enhances the precision and generalization. Key challenges include product variability, fermentation effects, calibration transfer, and the lack of publicly available datasets. To address these, future priorities should focus on developing portable instruments, applying multimodal data fusion and transfer learning, ensuring interpretable AI models, and establishing standardized protocols for routine, non-destructive yogurt authenticity testing. Hyperspectral imaging ; Identification (biology) ; Spectroscopy ; Quality (philosophy) ; Product (mathematics) ; Sensor fusion ; Chemometrics Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Identification and Quantification in Food
Authors: Ricardo Luiz Perez Teixeira
Source: Research Square (preprint, 2025-12-02)
Field: Materials Science — Metals and Alloys

Metallic Biomaterials: Insights into the Microstructure and Electrochemical Properties of AISI 316L Stainless Steel Microstructure ; Corrosion ; Pitting corrosion ; Phase (matter) ; Electrochemistry ; Metal ; Layer (electronics) Hydrogen embrittlement and corrosion behaviors in metals ; Titanium Alloys Microstructure and Properties ; Additive Manufacturing Materials and Processes
Authors: Yaoguo Lang | Mei Han | Jingyun Zhang | Chenxi Hu | Zhuoyi Pang | Yan Chen | C. Xue
Source: Research Square (preprint, 2025-09-29)
Field: Materials Science — Biomaterials

Voltage-Tunable Plasma-Activated Water: A Strategy for Combating Peri-Implantitis via Dual-Path Biofilm Disruption and Vascular Regeneration Biofilm ; Porphyromonas gingivalis ; Regeneration (biology) ; Confocal laser scanning microscopy ; Endothelial stem cell ; Viability assay ; Confocal ; Cell Electrospun Nanofibers in Biomedical Applications ; Bone Tissue Engineering Materials ; Laser Applications in Dentistry and Medicine
Authors: Mathieu Luisier | Nicolas Vetsch | Alexander Maeder | Vincent Maillou | Anders Winka | Leonard Deuschle | Chen Hao Xia | Manasa Kaniselvan | Marko Mladenović | Jiang Cao | Alexandros Nikolaos Ziogas
Source:  (article, 2025-09-24)
Field: Materials Science — Materials Chemistry

Acceleration of Atomistic NEGF: Algorithms, Parallelization, and Machine Learning The Non-equilibrium Green's function (NEGF) formalism is a particularly powerful method to simulate the quantum transport properties of nanoscale devices such as transistors, photo-diodes, or memory cells, in the ballistic limit of transport or in the presence of various scattering sources such as electronphonon, electron-photon, or even electron-electron interactions. The inclusion of all these mechanisms has been first demonstrated in small systems, composed of a few atoms, before being scaled up to larger structures made of thousands of atoms. Also, the accuracy of the models has kept improving, from empirical to fully ab-initio ones, e.g., density functional theory (DFT). This paper summarizes key (algorithmic) achievements that have allowed us to bring DFT+NEGF simulations closer to the dimensions and functionality of realistic systems. The possibility of leveraging graph neural networks and machine learning to speed up ab-initio device simulations is discussed as well. Formalism (music) ; Artificial neural network ; Limit (mathematics) ; Acceleration ; Quantum ; Key (lock) ; Scattering ; Graph Machine Learning in Materials Science ; Advancements in Semiconductor Devices and Circuit Design ; Quantum and electron transport phenomena
Authors: Thilo Bissbort | Kai-Uwe Hess | Elena Sturm | Sebastian Sturm | Detlef Rogalla | Ralf Dohmen | Knut Müller-Caspary | Donald B. Dingwell
Source: Chemical Geology (article, 2025-12-10)
Field: Materials Science — Ceramics and Composites

The glass transition and non-Arrhenian viscosity of extremely depolymerized silicate melts Properties of extremely depolymerized melts and glasses have always been elusive due to their inaccessibility in experiments. Information on viscosity, for example, was previously restricted to very high temperatures. Yet, theory and experimental details about peridotites suggest that highly depolymerized melts exhibit exceptional properties, such as extremely non-Arrhenian viscosity-temperature relationships. Studies at low temperatures, i.e., just above the glass transition temperature, required for investigating such phenomena have been impossible for many compositions due to their rapid crystallization and thus extremely poor glass-forming ability. Here, we have developed a novel method combining synthesis by pulsed laser deposition with characterization by fast differential scanning calorimetry (FDSC) to deposit glassy thin films, from a plasma directly onto FDSC sensors, combined with calorimetric analysis at very high heating-cooling rates (3500–9500 °C s −1 ). In this way we have been able to explore the glass transition of the glasses (at or near olivine stoichiometry): Mg 1.30 SiO 3.3 , Fe 0.34 Mg 1.67 SiO 4.0 , Fe 0.49 Mg 1.53 SiO 4.0 , Fe 1.21 Mg 0.91 SiO 4.1 , and Fe 2.26 SiO 4.3 . The peak glass transition temperature at 5000 °C s −1 increases from 550 °C for Fe 2.26 SiO 4.3 with decreasing (Fe + Mg)/Si and with increasing Mg/(Fe + Mg) to 736 °C for Mg 1.30 SiO 3.3 . Viscosities derived through the shift factor approach range from 10 5.61 to 10 6.04 Pa∙s in that temperature range. These results thus extend the experimentally available viscosity range by ca. seven log units and were employed here, together with high-temperature superliquidus viscosity data to fit the Vogel-Fulcher-Tammann (VFT) equations for these melts. The viscosity-temperature relationships are all strongly non-Arrhenian (m = 82–123 ± 15). We provide a comparison with other experimental studies of ultrabasic melts and with predictions from viscosity models. The inability of the latter to predict the observed viscosities highlights the necessity of direct experimental studies such as that presented here. Differential scanning calorimetry ; Glass transition ; Silicate ; Crystallization ; Viscosity ; Silicate glass ; Olivine ; Range (aeronautics) Glass properties and applications ; High-pressure geophysics and materials ; Geological and Geochemical Analysis