jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: An H. | Yasser Abdul Djawad | Edi Suhardi Rahman
Source: Jurnal Media Elektrik (article, 2025-04-25)
Field: Chemistry — Analytical Chemistry

BEEF QUALITY DETECTION APPLICATION USING CONVOLUTIONAL NEURAL NETWORK Beef consumption in Indonesia increases every year, but this high demand triggers traders to cheat by selling meat that is not fit for consumption, such as rotten meat. This endangers public health and raises concerns, such as the case of the discovery of rotten meat in Makassar. To help people recognise the quality of beef, this research develops a beef quality detection application using the Convolutional Neural Network (CNN) method with Edge Impulse Studio. This application was developed using a prototype model and Research and Development (R&amp;D) method at the Electrical Engineering Laboratory of Makassar State University. Tests showed a model accuracy rate of 99% and test accuracy of 99.17%. Evaluation based on ISO 25010 includes four aspects: functional suitability reached 100% according to the validator lecturer, usability with 97% response proportion, performance efficiency with ±85% CPU usage and 83.3 MB memory, and portability which shows the application runs well on various Android versions. Compatibility was tested by running the application simultaneously with other applications, and the results remained optimal. This application provides a practical solution to detect beef quality quickly and accurately, helping people prevent health risks due to consumption of unfit meat. Convolutional neural network ; Computer science ; Quality (philosophy) ; Artificial intelligence ; Pattern recognition (psychology) Spectroscopy and Chemometric Analyses
Authors: Owain T. Beynon | Adham Hashibon
Source: ArXiv.org (preprint, 2025-10-14)
Field: Materials Science — Materials Chemistry

Anharmonic Effects in Ge2Sb2Te5 and Consequences on Thermodynamic Stability Chalcogenide materials are an important class of phase change material (PCMs) owing to their employment in digital memory solutions. Chalgogenide materials have applications in phase change random access memory (PCRAM) due to their ability to reversibly cycle between crystalline and amorphous states, and of these materials Ge2Sb2Te5 (GST) is of particular interest due to its speed, stability and low crystallisation temperatures. GST possesses two stable crystalline polymorphs, cubic and hexagonal (trigonal system). Studies show that phenomena such as heat transport and thermal lattice expansion drive the phase-change nature of these materials. These phenomena are not incorporated in the harmonic approximation, which is a popular model for describing vibrations in solids. Through ab initio density functional theory (DFT), we computationally investigate the anharmonic behaviour of pristine GST, without vacancies or defects, while considering the various stacking models that exist and inclusion of van der Waals (vdW) interactions in our modelling. We present the vibrational analysis of different stacking models in GST; Petrov and Kooi-De Hosson (KDH) models and the quantification anharmonic behaviour. Our results demonstrate the importance of incorporating anharmonic and dispersion effects when modelling GST, especially in the choice of stacking models, along with implications for phenomena relating to phase-change behaviour. Anharmonicity ; Stacking ; van der Waals force ; Chalcogenide ; Amorphous solid ; Ab initio ; Phonon ; Thermal stability ; Density functional theory Phase-change materials and chalcogenides ; Chalcogenide Semiconductor Thin Films ; Advanced Thermoelectric Materials and Devices
Authors: Lisha Wang | Hui-Ping Wang | Chenchen Zhang | Wei Sun | Yue Wang | Lijuan Wang | Xiaoyan Kang
Source: Crystals (article, 2025-07-11)
Field: Materials Science — Biomaterials

Enhancing Electrochemical Kinetics and Stability of Biodegradable Mg-Y-Zn Alloys with LPSO Phases via Strategic Micro-Alloying with Ca, Sr, Mn, and Zr This study systematically investigated the effects of biologically relevant microalloying elements—calcium (Ca), strontium (Sr), manganese (Mn), and zirconium (Zr)—on the electrochemical behavior of Mg-Y-Zn alloys containing long-period stacking ordered (LPSO) phases. The alloys were prepared by casting and characterized using X-ray diffraction (XRD), optical microscopy (OM), and scanning electron microscopy with energy-dispersive spectroscopy (SEM/EDS). Electrochemical properties were assessed through potentiodynamic polarization in Hank’s solution, and corrosion rates were determined by hydrogen evolution and weight loss methods. Microalloying significantly enhanced the corrosion resistance of the base Mg-Y-Zn alloy, with corrosion rates decreasing from 2.67 mm/year (unalloyed) to 1.65 mm/year (Ca), 1.36 mm/year (Sr), 1.18 mm/year (Zr), and 1.02 mm/year (Mn). Ca and Sr additions introduced Mg2Ca and Mg17Sr2, while Mn and Zr refined the existing LPSO structure without new phases. Sr refined the LPSO phase and formed a uniformly distributed Mg17Sr2 network, promoting uniform corrosion and suppressing deep localized attacks. Ca-induced Mg2Ca acted as a temporary sacrificial phase, with corrosion eventually propagating along LPSO interfaces. The Mn-containing alloy exhibited the lowest corrosion rate; this is attributed to the suppression of both anodic and cathodic reaction kinetics and the formation of a stable protective surface film. Zr improved general corrosion resistance but increased susceptibility to localized attacks due to dislocation-rich zones. These findings elucidate the corrosion mechanisms in LPSO-containing Mg alloys and offer an effective strategy to enhance the electrochemical stability of biodegradable Mg-based implants. Kinetics ; Electrochemistry ; Materials science ; Metallurgy ; Electrochemical kinetics ; Chemical engineering ; Chemistry ; Electrode ; Physical chemistry ; Physics Magnesium Alloys: Properties and Applications ; Hydrogen Storage and Materials ; Corrosion Behavior and Inhibition
Authors: Yury Belousov | Brian Pulfer | Vitaliy Kinakh | Slava Voloshynovskiy
Source: ArXiv.org (preprint, 2025-05-21)
Field: Materials Science — Materials Chemistry

Beyond Classification: Evaluating Diffusion Denoised Smoothing for Security-Utility Trade off While foundation models demonstrate impressive performance across various tasks, they remain vulnerable to adversarial inputs. Current research explores various approaches to enhance model robustness, with Diffusion Denoised Smoothing emerging as a particularly promising technique. This method employs a pretrained diffusion model to preprocess inputs before model inference. Yet, its effectiveness remains largely unexplored beyond classification. We aim to address this gap by analyzing three datasets with four distinct downstream tasks under three different adversarial attack algorithms. Our findings reveal that while foundation models maintain resilience against conventional transformations, applying high-noise diffusion denoising to clean images without any distortions significantly degrades performance by as high as 57%. Low-noise diffusion settings preserve performance but fail to provide adequate protection across all attack types. Moreover, we introduce a novel attack strategy specifically targeting the diffusion process itself, capable of circumventing defenses in the low-noise regime. Our results suggest that the trade-off between adversarial robustness and performance remains a challenge to be addressed. Adversarial system ; Robustness (evolution) ; Smoothing ; Process (computing) ; Noise (video) ; Diffusion ; Diffusion process Fusion materials and technologies ; Advancements in Semiconductor Devices and Circuit Design ; Numerical methods in engineering
Authors: Javid Ali | Didar Abdulkhaleq Ali
Source: European Journal of Pure and Applied Mathematics (article, 2025-05-01)
Field: Chemistry — Spectroscopy

Stability and Maximum Independent Bond Set Polynomials of Painkiller Molecules Using Maximum Matching Chemical graph theory establishes a connection between the properties of molecules and their corresponding molecular graphs. A topological index is a graph invariant that characterizes the graph's structure and remains unaffected by graph automorphisms. In chemical graph theory, degree-based topological indices are particularly significant, offering crucial insights into the structural features of molecules. In this work, we introduce the maximum independent bond set polynomial $MIBSP(H;x,y)$, a powerful tool for deriving various degree-based topological indices. We specifically apply $MIBSP(H;x,y)$, to the chemical graphs of several painkiller molecules, including Aspirin, Paracetamol, Caffeine, Ibuprofen, Phenacetin, and Salicylic acid. The degree-based topological indices derived from these polynomials provide a deeper understanding of the molecular structures and their potential applications in pharmaceutical research. Mathematics ; Matching (statistics) ; Set (abstract data type) ; Stability (learning theory) ; Combinatorics ; Bond ; Applied mathematics ; Discrete mathematics ; Statistics ; Computer science Analytical Chemistry and Chromatography ; Chemical Thermodynamics and Molecular Structure ; Computational Drug Discovery Methods
Authors: Alexandra Roesch | Cornelia Hiemenz | Teresa M Findley | I. Goldberg | Roland Windisch | Christian Wichmann | Gideon Kersten | Tim Menzen
Source: European Journal of Pharmaceutics and Biopharmaceutics (article, 2025-03-28)
Field: Materials Science — Biomaterials

Stability of Jurkat cells during short-term liquid storage analyzed by flow imaging microscopy Jurkat cells ; Microscopy ; Term (time) ; Flow (mathematics) ; Chemistry ; Stability (learning theory) ; Nanotechnology ; Biomedical engineering ; Materials science ; Biophysics ; Biological system ; Optics ; Mechanics ; Physics ; Medicine ; Computer science ; Biology ; Machine learning ; Immunology ; Immune system Advanced Cellulose Research Studies
Authors: Tara Afra | Walter Fuscaldo | Dimitrios C. Zografopoulos | Teresa Natale | Francesco Dell’Olio
Source: Optical and Quantum Electronics (article, 2025-11-04)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Numerical study of multi-channel Terahertz sensors via all-dielectric metasurfaces supporting bics Abstract Metasurfaces that support bound states in the continuum (BICs) enable extreme spectral selectivity by suppressing radiative leakage; controlled symmetry breaking converts ideal BICs into quasi-BICs with finite yet ultra-high quality factors. Here, we design and numerically validate an all-dielectric silicon metasurface on SiO₂ that, under normal incidence, supports five quasi-BIC resonances in the 0.8–1.0 THz band. The metasurface unit cell comprises two semi-elliptical silicon elements; introducing a narrow slit in one element perturbs the symmetry and couples the otherwise dark modes to free space. Transmission and eigenmode analyses reveal five sharp resonance dips in the 0.8–1.0 THz band. We harness the quasi-BIC resonances excited under TE polarization for refractometric sensing by placing an analyte layer above the metasurface and quantify performance vs. analyte refractive index (1.4–1.5) and thickness (0–150 μm). The device attains a maximum sensitivity of 169.6 GHz RIU⁻¹ and a figure of merit (FOM) of 3.818 × 10 3 RIU −1 and a Q-factor of 3.42 × 10 4 owing to strong near-field confinement in the slit-perturbed element. The straightforward geometry, multi-resonant operation, and compatibility with dielectric microfabrication provide a practical route to multi-channel THz sensors. Our results establish design guidelines for engineering quasi-BICs at THz frequencies and for tailoring their spectral response to maximize sensing performance. Terahertz radiation ; Metamaterial ; Figure of merit ; Refractive index ; Polarization (electrochemistry) ; Silicon ; Normal mode ; Dielectric ; Coupled mode theory Metamaterials and Metasurfaces Applications ; Plasmonic and Surface Plasmon Research ; Terahertz technology and applications
Authors: Khushi Verma
Source: INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT (review, 2025-04-29)
Field: Chemistry — Analytical Chemistry

A Comprehensive Review on Different Food Adulteration with the Application of ATR-FTIR. Abstract The practice of food adulteration creates major health threats together with economic issues around the world. Researchers have examined how ATR-FTIR spectroscopy detects adulterants within five routine food products which include milk together with coffee along with honey and also ghee and oils. The presented review evaluates ATR-FTIR by examining recent publications about its functioning approaches together with its restrictions and forensic uses in food authentication fields. This study shows ATR-FTIR enables fast and non-destructive and highly sensitive assessment of adulterants throughout different food samples requiring minimal sample handling. The article investigates detection thresholds and shows the specific spectral elements that signify different adulterated substances while exploring traditional analytical techniques. The discussion ends by showcasing how ATR-FTIR shows promise for food safety and authentication regulation adoption which requires future research for specific knowledge acquisition. Keywords: - ATR-FTIR spectroscopy; food adulteration; forensic analysis; milk authenticity; honey detection; edible oils; rapid testing Fourier transform infrared spectroscopy ; Food science ; Business ; Chemistry ; Chemical engineering ; Engineering Spectroscopy and Chemometric Analyses ; Identification and Quantification in Food
Authors: Daniela Klaproth-Andrade | Yanik Bruns | Wassim Gabriel | Christian Nix | Valter Bergant | Andreas Pichlmair | Mathias Wilhelm | Julien Gagneur
Source: Molecular & Cellular Proteomics (article, 2025-12-25)
Field: Chemistry — Spectroscopy

Modanovo: A Unified Model for Post-translational Modification-Aware De Novo Sequencing Using Experimental Spectra From In Vivo and Synthetic Peptides Post-translational modifications (PTMs) play a central role in cellular regulation and are implicated in numerous diseases. Database searching remains the standard for identifying modified peptides from tandem mass spectra but is hindered by the combinatorial expansion of modification types and sites. De novo peptide sequencing offers an attractive alternative, yet existing methods remain limited to unmodified peptides or a narrow set of PTMs. Here, we curated a large dataset of spectra from endogenous and synthetic peptides from ProteomeTools spanning 19 biologically relevant amino acid-PTM combinations, covering phosphorylation, acetylation, and ubiquitination. We used this dataset to develop Modanovo, an extension of the Casanovo transformer architecture for de novo peptide sequencing. Modanovo achieved robust performance across these amino acid-PTM combinations (median area under the precision-coverage curve 0.92), while maintaining performance on unmodified peptides (0.93), nearly identical to Casanovo (0.94). The model outperformed π-PrimeNovo-PTM and InstaNovo-P and showed increased precision and complementarity to the database search tool MSFragger. Robustness was confirmed across independent datasets, particularly at peptide lengths frequently represented in the curated dataset. Applied to a phosphoproteomics dataset from monkeypox virus-infected cells, Modanovo recovered numerous confident peptides not reported by database search, including new viral phosphosites supported by spectral evidence, thereby demonstrating its complementarity to database-driven identification approaches. These results establish Modanovo as a broadly applicable model for comprehensive de novo sequencing of both modified and unmodified peptides. Peptide ; Phosphoproteomics ; Proteogenomics ; Complementarity (molecular biology) ; Synthetic biology ; Tandem mass spectrometry ; Robustness (evolution) ; Peptide library ; Complementarity determining region Advanced Proteomics Techniques and Applications ; vaccines and immunoinformatics approaches ; Machine Learning in Bioinformatics
Authors: B. Pratama
Source: Indonesian Journal of Innovation and Applied Sciences (IJIAS) (article, 2025-06-30)
Field: Materials Science — Materials Chemistry

Cost-Benefit Analysis of Nano Coating on Ceramic Insulators on Leakage Current Characteristics Ceramic insulator components have high popularity because the majority of electrical distribution systems in Indonesia use ceramic insulators. Ceramic insulators have advantages in mechanical strength, but the nature of the surface material makes it easy to absorb water, which can accelerate aging and increase leakage current when used in high-pollution areas. This research will test the effect of salt and fly ash contaminants on the leakage current characteristics of ceramic insulators. Furthermore, it will be tested the effect of nano coating on the leakage current characteristics of ceramic insulators. Tests were conducted to analyze the leakage current characteristics on the insulator surface due to the influence of contaminants by varying the ESDD and NSDD values of contaminants with light, medium, heavy, and very heavy categories on the ceramic insulator surface. The results show that the higher the NSDD level and ESDD level of salt and fly ash contaminants, will increase the leakage current value. The test results of nano-coated ceramic insulators prove that they can reduce the value of leakage current. In testing ceramic insulators coated with nano coating materials contaminated with salt with a very heavy pollutant category, it is proven that the leakage current value is reduced by 23.5%. Testing ceramic insulators coated with nano coating materials contaminated with fly ash with a very heavy pollutant category proved that the leakage current value was reduced by 23.7%. Benefit-cost analysis is used to assess whether the nano-coating project on ceramic insulators is feasible or not feasible. The results of the analysis show that the Benefit Cost Ratio (BCR) value is obtained from the total present value benefit divided by the total present value cost, so that the result is 1&lt; or more than one. The nano-coating project on ceramic insulators from the BCR parameter is feasible to implement. Coating ; Materials science ; Leakage (economics) ; Nano- ; Ceramic ; Composite material ; Nanotechnology High voltage insulation and dielectric phenomena ; Elevator Systems and Control
Authors: Orfeú Mouret | Jad Abbass
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2025-06-26)
Field: Chemistry — Spectroscopy

CATHe2: Enhanced CATH Superfamily Detection Using ProstT5 and Structural Alphabets Abstract Motivation The CATH database is a free publicly available online resource that provides annotations about the evolutionary and structural relationships of protein domains. Due to the flux of protein structures coming mainly from the recent breakthrough of AlphaFold and therefore the non-feasibility of manual intervention, the CATH team recently developed an automatic CATH superfamily classifier called CATHe, that uses a feed-forward network classifier with protein Language Model (pLM) embeddings as input. Using the same dataset, in this paper, we present, CATHe2 that improves on CATHe by switching the old pLM ProtT5 for one of the most recent versions called ProstT5, and by introducing domain 3D information as input to the classifier, in the form of Structural Alphabet representation, namely 3Di sequence embeddings. Finally, CATHe2 implements a new version of the feed-forward network (FNN, i.e, non-recurrent neural network) classifier architecture, fine-tuned to perform at the CATH superfamily prediction task. Results The best CATHe2 model reaches an accuracy of 92.2 ± 0.7% with an F1 score of 82.3 ± 1.3% which constitutes an improvement of 9.9% on the F1 score and 6.6% on the accuracy, from the previous CATHe version (85.6 ± 0.4% accuracy and 72.4 ± 0.7% F1 score) on its largest dataset (~ 1700 superfamilies). This model uses ProstT5 AA sequence and 3Di sequence embeddings as input to the classifier, but a simplified version requiring only AA sequences, already improves CATHe’s F1 score by 6.7 ± 1.3% and accuracy by 6.6 ± 0.7% on its largest dataset. Availability &amp; Implementation The code is available on https://GitHub.com/Mouret-Orfeu/CATHe2 . Datasets: https://doi.org/10.5281/zenodo.14534966 Contact orfeu.mouret.pro@outlook.fr , j.abbass@kingston.ac.uk SUPERFAMILY ; Computational biology ; Computer science ; Biology ; Genetics ; Gene Advanced Proteomics Techniques and Applications ; Machine Learning in Bioinformatics ; Epigenetics and DNA Methylation
Authors: Hiroo Suzuki | Y. SENDA | Kaoru Hisama | S. Aso | Yuta Takahashi | Shun Fujii | Yasuhiko Hayashi
Source: Advanced Science (article, 2025-12-12)
Field: Materials Science — Materials Chemistry

Inside the Microreactor: In Situ Real‐Time Observation of Vapor–Liquid–Solid Growth of Monolayer TMDCs Real-time observation of molten droplet-driven crystal growth provides an unprecedented in situ window into the formation of atomically thin transition metal dichalcogenides (TMDCs). Materials such as MoS<sub>2</sub> and WS<sub>2</sub> exhibit remarkable optoelectronic properties arising from their monolayer structures, enabling advanced applications that exploit valley degrees of freedom. Among various synthetic approaches, vapor-liquid-solid (VLS) growth from a low-melting molten source containing alkali, transition metal, halide, and oxygen atoms has proven highly effective for producing large single-crystal monolayer TMDCs, while also yielding distinct growth regimes including molten particle-driven nanoribbon formation. A chemical vapor deposition method is recently developed that integrates VLS growth with the spatial confinement provided by a substrate-stacked microreactor; however, the precise role of confinement and droplet dynamics remains unclear. Here, in situ the VLS growth of TMDCs inside such microreactors is directly captured using an infrared heating furnace. The microreactor, formed by sealing a transparent sapphire substrate with a Na<sub>2</sub>WO<sub>4</sub>-coated SiO<sub>2</sub>/Si wafer, enables continuous observation of growth mode transitions governed by the balance of sulfur and Na<sub>2</sub>WO<sub>4</sub> supply. The findings demonstrate that fine control over precursor supply rates is essential for engineering the size, morphology, and crystallinity of monolayer TMDCs in the VLS regime. Monolayer ; In situ ; Sapphire ; Transition metal ; Chemical vapor deposition ; Substrate (aquarium) ; Crystal growth ; Crystallinity 2D Materials and Applications ; Transition Metal Oxide Nanomaterials ; Graphene research and applications
Authors: Carl G. Henning Hansen | Simone Orioli | Kresten Lindorff‐Larsen
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2025-07-22)
Field: Chemistry — Physical and Theoretical Chemistry

A Bayesian approach to interpret time-resolved experiments using molecular simulations Time-resolved experiments can provide unique insights into dynamical processes such as protein folding, ligand binding, and many other molecular processes. These experiments are, however, difficult to interpret at the molecular level because they generally report on signals that are averaged over multiple configurational states, and because they often report on processes that are well beyond what can be studied using most simulation methods. Here we describe an approach to use molecular simulations to model and interpret time-resolved experiments. The method, which we term trBME (time-resolved Bayesian/Maximum Entropy), is based on combining a model for the dynamics of the system, with 3D structural models of the protein that can be compared to the experiments. We illustrate the utility of the model using synthetic time-resolved small-angle X-ray scattering data and show how it can be used to extract detailed information from the experimental data on the process of protein unfolding. Bayesian probability ; Computer science ; Bayesian inference ; Econometrics ; Statistical physics ; Artificial intelligence ; Mathematics ; Physics Various Chemistry Research Topics ; Computational Drug Discovery Methods ; Metabolomics and Mass Spectrometry Studies