jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Seema Ramniwas | Girish Kumar | Ramesh Kumar
Source: Biotechnology for Sustainable Materials (article, 2025-12-21)
Field: Materials Science — Biomaterials

Yeast-based nanofibers technologies for sustainable fruit fly pest management: a review Numerous species within the family Tephritidae are major agricultural pests and infest a broad array of fruits and vegetables in tropical and subtropical regions. As a result, they pose a serious threat to global fruit production. Traditional pest management techniques often face limitations due to their harmful effects on the environment and reduced efficacy in variable field conditions such as rain, humidity, temperature, and light. Recently, electrospinning technology has demonstrated remarkable potential in the preparation of sustained carriers. While several review articles have addressed the use of electrospun nanofibers for pest control, no comprehensive review has specifically focused on yeast-based nanofibers in fruit fly management. This review highlights the significance of yeast-based nanofibers produced via electrospinning as a promising eco-friendly approach for fruit fly pest management. The unique properties of electrospun nanofibers such as large surface area, high porosity, and controlled release capabilities, improve the stability, longevity, and delivery efficiency of yeast-derived attractants. This novel approach addresses the issue of rapid volatilization and environmental breakdown of volatile compounds, prolonging the shelf life and increasing the field stability of biological attractants. This review synthesizes current knowledge yeast metabolism, genetic engineering of yeast for pest control, and the advancements in electrospinning for controlled release. The technical considerations are critically discussed, including polymer and solvent selection, and the impact of electrospinning on yeast viability and morphology. While emphasizing the multidisciplinary promise of this approach, we also identify key research gaps and future directions, such as optimizing encapsulation protocols, validating field performance, and addressing potential ecotoxicological impacts, to accelerate the translation of yeast-based nanofiber technology into practical, scalable, and sustainable solutions for fruit fly control. Electrospinning ; Integrated pest management ; Nanofiber ; PEST analysis ; Pest control Electrospun Nanofibers in Biomedical Applications ; Nanocomposite Films for Food Packaging ; Insect behavior and control techniques
Authors: Wenjie Xiong | Hao Huang | Yu Wu | Xinji Xu | Geng Li | Zonglin Gu | Shuming Zeng
Source: ArXiv.org (preprint, 2025-04-03)
Field: Materials Science — Materials Chemistry

Decoupling Coherent and Particle-like Phonon Transport through Bonding Hierarchy in Soft Superionic Crystals Within the framework of the unified theory thermal transport model, the competing contributions of coherent and incoherent terms create a trade-off relationship, posing substantial challenges to achieving a reduction in overall $\rm κ_L$. In this work, we theoretically demonstrate that the superionic crystals X$_6$Re$_6$S$_8$I$_8$ (X = Rb, Cs) exhibit ultralow glass-like and particle-like thermal conductivities. The weak interactions between free alkali metal ions X$^+$ (X = Rb, Cs) and I$^-$ anions induce pronounced lattice anharmonicity, which enhances phonon scattering and suppresses group velocities, thereby reducing the particle-like thermal conductivity ($\rm κ_p$). Concurrently, the significant bonding heterogeneity within the [Re$_6$S$_8$I$_6$]$^{4-}$ clusters promotes phonon dispersion flattening and low-frequency phonon localization. The resulting discretized phonon flat bands substantially diminish the glass-like thermal conductivity ($\rm κ_c$). At room temperature, the total $\rm κ_L$ of X$_6$Re$_6$S$_8$I$_8$ (X = Rb, Cs) falls below 0.2 Wm$^{-1}$K$^{-1}$. Furthermore, the bonding characteristics between X$^+$ and I$^{-1}$ anions induce an anomalous cation mass-independent stiffening of low-frequency phonon branches in this system, resulting in counterintuitive thermal transport behavior. This work elucidates fundamental mechanisms governing heat transfer in ultralow $\rm κ_L$ materials and establishes novel pathways for transcending conventional thermal conductivity limitations. Decoupling (probability) ; Phonon ; Hierarchy ; Condensed matter physics ; Particle (ecology) ; Materials science ; Chemical physics ; Statistical physics ; Physics ; Engineering ; Law Thermal properties of materials
Authors: Anne Seidel | Erica Brendler | Ana Torvisco | Roland C. Fischer | Jörg Wagler
Source: Molecules (article, 2025-08-26)
Field: Chemistry — Inorganic Chemistry

Lewis Acid–Base Adducts of α-Amino Isobutyric Acid-Derived Silaheterocycles and Amines The 1:1 stoichiometric reactions of α-amino isobutyric acid (H<sub>2</sub>Aib) and diaminosilanes of the type Si<i>RR</i>'(N<i>R</i><sup>1</sup><i>R</i><sup>2</sup>)<sub>2</sub> (SiMe<sub>2</sub>(imidazol-1-yl)<sub>2</sub>, SiMe<sub>2</sub>(NH<i>n</i>Pr)<sub>2</sub>, and Si<i>RR</i>'(pyrrolidin-1-yl)<sub>2</sub> with <i>R</i>,<i>R</i>' = Me,Me, Me,H, Me,Vi, and Et,Et) afforded the pentacoordinate silicon complexes (Aib)Si<i>RR</i>'(HN<i>R</i><sup>1</sup><i>R</i><sup>2</sup>) with the release of one equivalent of HN<i>R</i><sup>1</sup><i>R</i><sup>2</sup>. Single-crystal X-ray diffraction analyses confirmed the coordination of the N-donor Lewis base (i.e., imidazole, <i>n</i>-propylamine, and pyrrolidine, respectively) in an axial position of the distorted trigonal-bipyramidal Si-coordination sphere, <i>trans</i> to the carboxylate O atom of the Si-chelating Aib-dianion. The N-H moieties of the adduct-forming Lewis bases are involved in N-H⋯O hydrogen bonds with carboxylate groups of adjacent complex molecules, thus supporting the supramolecular structures of these adducts. The equatorially bound NH group of the Aib-dianion is involved in N-H⋯O hydrogen bonds in most cases, and it gives rise to residual dipolar coupling of the <sup>14</sup>N nucleus with its directly bound atoms C and Si, thus causing characteristic shapes of both the <sup>29</sup>Si and <sup>13</sup>C NMR signals of these two atoms in the solid-state spectra. In contrast to the adduct-formation reactions, the analogous conversion of H<sub>2</sub>Aib and SiMe<sub>2</sub>(NH<i>t</i>Bu)<sub>2</sub> did not afford an amine adduct. Instead, a second equivalent of H<sub>2</sub>Aib entered the reaction, and the ionic silicon complex [<i>t</i>BuNH<sub>3</sub>]<sup>+</sup>[(Aib)<sub>2</sub>SiMe]<sup>-</sup> was obtained and characterized by crystallography and solution NMR spectroscopy. Isobutyric acid ; Lewis acids and bases ; Adduct ; Chemistry ; Base (topology) ; Amino acid ; Organic chemistry ; Stereochemistry ; Biochemistry ; Catalysis Synthesis and characterization of novel inorganic/organometallic compounds ; Organoboron and organosilicon chemistry ; Silicone and Siloxane Chemistry
Authors: Xiaowei Huang | Zexiang Li | Zhihua Li | Jiyong Shi | Ning Zhang | Qin Zhou | Liuzi Du | Tingting Shen | Roujia Zhang
Source: Foods (review, 2025-07-16)
Field: Chemistry — Analytical Chemistry

Application of Image Computing in Non-Destructive Detection of Chinese Cuisine Food quality and safety are paramount in preserving the culinary authenticity and cultural integrity of Chinese cuisine, characterized by intricate ingredient combinations, diverse cooking techniques (e.g., stir-frying, steaming, and braising), and region-specific flavor profiles. Traditional non-destructive detection methods often struggle with the unique challenges posed by Chinese dishes, including complex textural variations in staple foods (e.g., noodles, dumplings), layered seasoning compositions (e.g., soy sauce, Sichuan peppercorns), and oil-rich cooking media. This study pioneers a hyperspectral imaging framework enhanced with domain-specific deep learning algorithms (spatial-spectral convolutional networks with attention mechanisms) to address these challenges. Our approach effectively deciphers the subtle spectral fingerprints of Chinese-specific ingredients (e.g., fermented black beans, lotus root) and quantifies critical quality indicators, achieving an average classification accuracy of 97.8% across 15 major Chinese dish categories. Specifically, the model demonstrates high precision in quantifying chili oil content in Mapo Tofu with a Mean Absolute Error (MAE) of 0.43% w/w and assessing freshness gradients in Cantonese dim sum (Shrimp Har Gow) with a classification accuracy of 95.2% for three distinct freshness levels. This approach leverages the detailed spectral information provided by hyperspectral imaging to automate the classification and detection of Chinese dishes, significantly improving both the accuracy of image-based food classification by >15 percentage points compared to traditional RGB methods and enhancing food quality safety assessment. Hyperspectral imaging ; RGB color model ; Artificial intelligence ; Flavor ; Steaming ; Seasoning ; Computer science ; Food science ; Mathematics ; Pattern recognition (psychology) ; Raw material ; Chemistry Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Phytochemicals and Antioxidant Activities
Authors: Yingpeng Huo | Rongfei Liu | Guoyun Liu | Q.-L. Zhou | Yanna Lv | Jinxing Hu
Source: Research Square (preprint, 2025-10-28)
Field: Chemistry — Organic Chemistry

Copper-catalyzed decarboxylation of α,α-difluoroarylacetic acid with 1,2,4-triazine-3,5(2H,4H)-dione Decarboxylation ; Substrate (aquarium) ; Functional group ; Group (periodic table) ; Coupling (piping) ; Scope (computer science) Radical Photochemical Reactions ; Catalytic C–H Functionalization Methods ; Fluorine in Organic Chemistry
Authors: Kyle Noah D Esguerra | Juan Carlos Valladares de la Cruz | John Dominic L Sapao | Ricardo Fernández
Source: Cureus Journal of Engineering. (article, 2025-09-01)
Field: Materials Science — Materials Chemistry

Simulation, Fabrication, and Characterization of Bismuth Oxide-Ethylene Vinyl Acetate (Bi2O3-EVA) Composite As a Cesium-137 Gamma Radiation Shield Prolonged exposure to cesium-137 (Cs-137) gamma radiation, a high-energy source widely used in medicine, is known to cause cellular damage and increased rates of cancer. Wearable lead-lined radiation shields provide a direct protective barrier that attenuates ionizing radiation. Aside from being toxic, lead is heavy and rigid-this limits the dexterity of medical workers. Non-toxic polymer composites are promising solutions as they combine high attenuation rates of high-density materials with the lightness and flexibility of polymers. In this study, a composite consisting of bismuth oxide and ethylene vinyl acetate (Bi2O3-EVA) was simulated, fabricated, and characterized to determine its structural and radiation shielding performance. The simulation results determined that increasing Bi2O3 filler content enhances shielding performance. Bi2O3-EVA composites were then fabricated through melt-mixing methods and compression molding. Radiation shield testing showed that composites with higher Bi2O3 filler content displayed improved shielding efficiencies, with higher linear attenuation coefficients and lower half-value layer thicknesses. Samples consisting of 60% Bi2O3 by weight produced optimal shielding performance, achieving half-value layer values between 1.508 cm and 1.878 cm. Analysis of homogeneity levels through the ISO-based rapid comparative method and texture analysis yielded acceptable values, entailing that its consistent structure does not compromise radiation shielding performance. Moreover, the tensile strength is maintained even at a high 80% Bi2O3 by weight. The positive results obtained in this study present new opportunities for developing lighter and more flexible wearable medical gamma radiation shields that effectively attenuate gamma radiation. Bismuth ; Fabrication ; Composite number ; Materials science ; Characterization (materials science) ; Ethylene oxide ; Caesium ; Ethylene-vinyl acetate ; Oxide ; Nuclear chemistry ; Composite material ; Radiochemistry ; Chemistry ; Inorganic chemistry ; Nanotechnology ; Metallurgy ; Medicine Radiation Shielding Materials Analysis ; Graphite, nuclear technology, radiation studies ; Nuclear materials and radiation effects
Authors: Samuel Furones | Raúl Grau | José Manuel Barat Baviera | Samuel Verdú
Source: Food and Bioprocess Technology (article, 2025-02-12)
Field: Materials Science — Materials Chemistry

Evolution of Physicochemical and Toxicological Properties During Aqueous Exposition of the Nanoparticulated Food Colours E-174 and E-172: Study in Caenorhabditis elegans Caenorhabditis elegans ; Exposition (narrative) ; Chemistry ; Aqueous solution ; Caenorhabditis ; Food science ; Biochemistry ; Art ; Organic chemistry ; Gene Nanoparticles: synthesis and applications ; Dye analysis and toxicity
Authors: Piotr Wiśniowski | Magdalena Zimakowska-Laskowska | P. Mazuruk | Sławomir Taubert | Michał Stankiewicz
Source: Energies (article, 2025-04-12)
Field: Materials Science — Materials Chemistry

Optimization and Performance Evaluation of Diesel Oxidation Catalysts for Methane Removal in Dual-Fuel Diesel–CNG Engines Compressed natural gas (CNG) in dual-fuel diesel engines offers environmental benefits but significantly increases unburned methane (CH4) emissions, especially at low engine loads. This study investigates the effectiveness of different catalytic converters in methane oxidation under transient test conditions (WHTC). Three types of catalysts (Pt-, Rh-, and Pd-based) were evaluated using a combined approach of empirical engine bench tests and mathematical modelling. The results showed that, under actual exhaust gas temperature conditions, the average methane conversion efficiencies were 3.7% for Pt, 17.7% for Rh, and 31.3% for Pd catalysts. Increasing the exhaust gas temperature by 50% improved the conversion efficiencies to 7.3%, 51.8%, and 69.2%, respectively. Despite this enhancement, none of the catalysts reached the 90% efficiency threshold required to increase the CNG content of the fuel beyond 6% without exceeding emission limits. The results highlight the need for high-activity Pd-based catalysts and optimised thermal management strategies to enable the broader adoption of dual-fuel engines, while complying with Euro VI standards. Diesel fuel ; Dual (grammatical number) ; Methane ; Catalysis ; Diesel exhaust fluid ; Waste management ; Environmental science ; Diesel engine ; Automotive engineering ; Chemistry ; Diesel exhaust ; Engineering ; Organic chemistry Catalytic Processes in Materials Science ; Catalysis and Oxidation Reactions ; Advanced Combustion Engine Technologies
Authors: Mohammed Almomtan | Emad Al Ibrahim | Aamir Farooq
Source: ArXiv.org (preprint, 2025-06-02)
Field: Materials Science — Materials Chemistry

Fuelprop: Fuel property prediction from ATR-FTIR spectroscopic data Synthetic fuels are crucial for decarbonizing the transportation sector. A significant challenge lies in the rapid and efficient characterization of these fuels. Chemometric methods using ATR-FTIR data offer a potential alternative to conventional techniques. This study expands the applicability and performance of chemometric models by providing an extensive ATR-FTIR spectral dataset and exploring various data enhancement strategies. Data enhancement was achieved by semi-supervised data generation, consistency enforcement through unsupervised data augmentation, and data imputation using synthetic spectra blending and pseudo-labeling. Models were trained on surrogate fuels and rigorously tested on real fuels, representing out-of-distribution testing conditions. We believe that this work will enhance the adoption of chemometric models for fuel characterization. Property (philosophy) ; Consistency (knowledge bases) ; Imputation (statistics) ; Missing data ; Experimental data ; Predictive modelling ; Synthetic data ; Chemometrics Nuclear Materials and Properties ; Heat transfer and supercritical fluids ; Petroleum Processing and Analysis
Authors: Cristina Gentili | Christofer Hierold | Cosmin Roman
Source: Applied Intelligence (article, 2025-10-30)
Field: Materials Science — Materials Chemistry

Physics-informed autoencoder for feature extraction from multidimensional sensor measurement data Abstract In this work, we propose a physics-informed autoencoder, named PIAE, to extract features and reduce the dimensionality of sensor measurement data. This data consists of transistor transfer characteristics, that are drain current vs gate voltage measurements ( $${I_D}$$ - $${V_{GS}}$$ ) of carbon nanotube field-effect transistors (CNT-FETs) acquired during several gas-sensing experiments. The encoder of the PIAE is trained—via an additional loss term—to output four physically interpretable key features. These features correspond to typical transistor parameters: threshold voltage, subthreshold swing, transconductance, and ON-state current. The decoder module then reconstructs the CNT-FET transfer characteristics from these four key features. We evaluated our method on five different gas measurement runs performed with three distinct CNT-FET devices. The PIAE outperforms both a four-component principal component analysis and a four-parameter compact model in terms of reconstruction accuracy, achieving an average improvement of approximately 50% in the median root mean square reconstruction error. Moreover, the PIAE reaches a reconstruction accuracy comparable to that of a standard autoencoder while providing physical interpretability of the extracted features, opening new opportunities for sensor signal processing and analysis. Autoencoder ; Interpretability ; Pattern recognition (psychology) ; Encoder ; Feature extraction ; Transistor ; Curse of dimensionality ; Feature (linguistics) ; Principal component analysis Carbon Nanotubes in Composites ; Mechanical and Optical Resonators ; Gas Sensing Nanomaterials and Sensors
Authors: Ahmed Abuali | D. Clarke | M. Hjorth‐Jensen | Ioannis Konstantinidis | Claudia Ratti | Jianyi Yang
Source: arXiv (Cornell University) (preprint, 2025-01-09)
Field: Materials Science — Materials Chemistry

Deep learning of phase transitions with minimal examples Over the past several years, there have been many studies demonstrating the ability of deep neural networks to identify phase transitions in many physical systems, notably in classical statistical physics systems. One often finds that the prediction of deep learning methods trained on many ensembles below and above the critical temperature $T_{\rm c}$ behaves similarly to an order parameter, and this analogy has been successfully used to locate $T_{\rm c}$ and estimate universal critical exponents. In this work, we pay particular attention to the ability of a convolutional neural network to capture these critical parameters for the 2-$d$ Ising model when the network is trained on configurations at $T=0$ and $T=\infty$ only. We directly compare its output to the same network trained at multiple temperatures below and above $T_{\rm c}$ to gain understanding of how this extreme restriction of training data can impact a neural network's ability to classify phases. We find that the network trained on two temperatures is still able to identify $T_{\rm c}$ and $ν$, while the extraction of $γ$ becomes more challenging. Phase (matter) ; Artificial intelligence ; Computer science ; Psychology ; Physics ; Quantum mechanics Machine Learning in Materials Science
Authors: Nikodemos Hughes | Charles Morgenstern | Amanda Morgenstern
Source: Research Square (preprint, 2025-08-29)
Field: Chemistry — Physical and Theoretical Chemistry

Understanding the Effects of Electric Fields on Molecules through Gradient Path Curvature Curvature ; Path (computing) ; Electric field ; Physics ; Geometry ; Mathematics ; Classical mechanics ; Computer science ; Quantum mechanics Various Chemistry Research Topics ; Atomic and Molecular Physics ; Mass Spectrometry Techniques and Applications
Authors: О. В. Єфімов | Т.V. Potanina | М.М. Pylypenko | Vadym Kondratyuk | Ruslan Mygushchenko | І. Д. Сидоркін
Source: Nuclear and Radiation Safety (article, 2025-12-17)
Field: Materials Science — Metals and Alloys

Моделювання залежностей корозійної резистентності від легуючого елемента Fe цирконієвих оболонок твелів ядерних реакторів АЕС Подальший розвиток ядерної енергетики значною мірою залежить від розроблення передових конструкційних матеріалів для реакторів нового покоління та вдосконалення матеріалів для діючих атомних електростанцій. Високі вимоги до конструкційних матеріалів щодо вмісту домішкових елементів, зокрема заліза, можуть бути виконані лише за умови використання високочистих металів як вихідних компонентів та застосування нових технологій їхнього виробництва. У статті представлені основні результати досліджень з моделювання залежностей корозійної резистентності від легуючого елемента цирконієвих сплавів, на прикладі вітчизняного цирконієвого сплаву Zr1%Nb, властивості якого протягом років досліджувалися в Національному науковому центрі «Харківський фізико-технічний інститут» Національної академії наук України , для елементів активних зон існуючих та перспективних реакторів з водою під тиском. Наведено огляд і деякі результати сучасних досліджень науковців-ядерників щодо вивчення процесів корозії в цирконієвих сплавах різного складу (Zr1%Nb, Zircaloy-4, Zr-1,5%Sn та ін.), які застосовуються як конструкційні матеріали елементів активних зон ядерних реакторів типу ВВЕР, PWR, CANDU, BWR. Також наведено аналіз робіт щодо моделювання і розуміння процесу корозії, яка є багатомасштабним, багатофазним, багатопараметричним процесом. Легування цирконієвих сплавів з Fe є перспективним у розробленні технології вітчизняного виробництва матеріалів для облицювання тепловидільних елементів реакторів з високою надійністю і безпекою, що дуже важливо для розвитку атомної промисловості України. Розглянуто метод оптимізації кількості легуючого елемента Fe в конструкційних цирконієвих сплавах на прикладі українського сплаву Zr1%Nb на основі інтервального аналізу. Результати моделювання показали, що поліноміальна регресія, заснована на ймовірнісних оцінках та застосуванні методу найменших квадратів для визначення оптимальної кількості вмісту заліза Fe в цирконієвому сплаві Zr1%Nb з метою мінімізації швидкості утворення корозії, не завжди дає достовірні результати. Також інтервальна регресія, створена за цією концепцією, не у всіх випадках вирішує проблему. Прийшли до висновку, що перспективним ефективним підходом до побудови достовірної моделі визначення оптимальної кількості вмісту заліза Fe в цирконієвих сплавах оболонок твелів (на прикладі сплаву Zr1%Nb) з метою мінімізації швидкості утворення корозії є застосування алгоритмів чисельних методів інтервального аналізу в умовах обмежених похибок у вхідній інформації результатів випробувань та відсутності ймовірнісних характеристик цих похибок. Work (physics) ; Yield (engineering) ; Process (computing) ; Phase (matter) Hydrogen embrittlement and corrosion behaviors in metals ; Nuclear Materials and Properties ; Material Properties and Failure Mechanisms