jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Haiyan Hu | Yinan Zhu | Shanwen Chen | Qianyi Huang | Qian Zhang
Source: Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies (article, 2025-09-03)
Field: Chemistry — Analytical Chemistry

FruitPhone: Detecting Sugar Content in Fruits Using Unmodified Smartphones with Spectral Imaging Developing an accessible fruit sugar content detection system for average users, particularly diabetic patients, is essential. Current fruit sugar detection solutions often fail to balance ease of use with high accuracy. In this study, we introduce FruitPhone, an innovative smartphone-based spectral imaging system that provides quantitative assessments of fruit sugar content. To enable the smartphone to capture the fine-grained spectral features necessary for accurate sugar content analysis, FruitPhone utilizes the smartphone screen to simulate multiple monochromatic light sources. This approach allows for the collection of spectral data that extends beyond the standard three-channel RGB format. To address the discrepancies between screen-generated and true monochromatic light, we propose a two-stage spectral reconstruction algorithm that translates the smartphone's pseudo-spectral data into real multi-spectral images for further analysis. Additionally, we tackle environmental variability by employing a black screen light for reference data collection, which helps mitigate ambient influences and enhance consistency. We also introduce a detrending algorithm for high-dimensional spectral images to correct spatial inconsistencies while preserving spectral trends. We evaluate FruitPhone using 335 spectral images from 37 different fruit types, achieving a normalized mean-absolute error of 8.48% °Bx, which is comparable to that of a laboratory spectral system and demonstrates a 19.98% reduction in reconstruction errors, along with a 45.58% R2 improvement over baseline schemes. Sugar ; Content (measure theory) ; Spectral imaging ; Food science ; Chemistry ; Computer vision ; Remote sensing ; Computer science ; Mathematics ; Geography Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Identification and Quantification in Food
Authors: Simona Greculeasa | O. Crisan
Source: Magnetochemistry (article, 2025-11-11)
Field: Materials Science — Materials Chemistry

Rare-Earth-Free Exchange-Coupled Nanocomposites Based on M-Type Hexaferrites Efficient permanent magnets that are concomitantly economically viable are of paramount importance for allowing industrial stakeholders to maintain a growing and competitive advantage. This study provides a comprehensive overview of recent developments in the field of rare-earth-free nanocomposite permanent magnets based on hexaferrites. The basic phenomenology of exchange-spring-coupled nanocomposites, comprising hard and soft magnetic components, is thoroughly explained. The use of hexaferrites as a hard phase, serving as a viable alternative to rare-earth-based permanent magnets, is extensively discussed, taking economical, accessibility-related, and environmental aspects into consideration. State-of-the-Art architectures of hard–soft magnetic nanocomposites based on hexaferrites as the hard magnetic phase, ranging from typical nanocomposites to nanowire arrays and special core–shell-like morphologies, are explored in detail. The maximum energy product (BH)max, representing the quality indicator for permanent magnets, is investigated by taking into consideration various degrees of freedom, such as substitutions, geometry, size, shape, preparation, and processing conditions (annealing), volume fraction of magnetic phases, and interfaces. Promising strategies to overcome the present challenges (e.g., size control, coercivity–remanence trade-off, and optimization for large-scale production) are provided within the framework of future permanent magnet design. Magnet ; Nanocomposite ; Magnetic field ; Volume fraction ; Phenomenology (philosophy) ; Ranging Magnetic Properties and Synthesis of Ferrites ; Electromagnetic wave absorption materials ; Magnetic Properties of Alloys
Authors: Yifei Yue | Athulya S. Palakkal | N. Duane Loh | Jianwen Jiang
Source: ChemRxiv (preprint, 2025-04-25)
Field: Chemistry — Inorganic Chemistry

Are Hemilabile Metal−Organic Frameworks Overlooked as Promising Water-Stable Adsorbents? Elucidating Their Physical and Hydrolytic Properties Using Machine-Learned Potentials Poor water stability of many metal–organic frameworks (MOFs) remains a persistent bottleneck toward their practical applications. Recently, hemilabile STAMs experimentally demonstrate much stronger water stability, as well as improved adsorption performance under humid conditions, than the compositionally similar HKUST-1. Yet, the fundamental properties of STAMs remain largely unexplored. Herein, we apply the density-functional theory (DFT) calculations and machine-learned potentials (MLP) based molecular dynamics (MD) simulations to evaluate the physical and hydrolytic properties of STAMs functionalized with various hydrophobic/hydrophilic groups. Encouragingly, STAMs are predicted to have high mechanical strength and low heat capacity, which are desirable attributes in adsorption processes. Using MLP-MD simulations, we reveal that the Cu-paddlewheels in STAMs are generally stable even at a high water loading, despite certain defects. Moreover, we elucidate the effect of tuning the hydrophobicity of functional groups on water stability and dynamics. Particularly, hydrophobic groups impede the formation of energetically stable water pentamer and water diffusion in the hexagonal pores of STAMs. This indicates that the adsorption selectivity of polar (hydrophilic) or nonpolar (hydrophobic) solvents in the hexagonal pores can be tailored by modifying the hydrophobicity of functional groups, while adsorption in the triangular pores remains unaffected. More generally, beyond evidencing that STAMs are promising adsorbents in carbon capture and water-harvesting applications, we provide microscopic insights into hydrolytic properties, water stability and dynamics in STAMs that can potentially facilitate the design of new water-stable MOFs. Adsorption ; Hydrolysis ; Metal-organic framework ; Metal ; Chemistry ; Organic chemistry Metal-Organic Frameworks: Synthesis and Applications
Authors: Vaishnavi Kadali | Prashant Unde | Laxmikant Borse
Source: Biosciences Biotechnology Research Asia (article, 2025-12-30)
Field: Chemistry — Analytical Chemistry

A Comprehensive Review of Advancing Pharmaceutical Analysis Technology Through Chemometrics Approaches ABSTRACT: In today’s pharmaceutical world, where advanced instruments like NIR, FTIR, Raman, and HPLC generate huge volumes of complex data, chemometrics has become an essential tool. It combines statistics, mathematics, and computer science to help scientists understand and make use of this data in smarter ways. This review highlights how chemometric techniques are being used to improve pharmaceutical analysis, from raw materials to final products. These tools help overcome challenges in traditional methods, such as overlapping spectra or detecting ingredients present in very low concentrations. Chemometrics is also making routine tasks like calibration curve generation, impurity profiling, and dissolution studies faster, more accurate, and less dependent on complex separations. It supports techniques like UV-Vis, NIR, and NMR spectroscopy, and even helps with optimizing eco-friendly methods in line with green chemistry goals. Overall, chemometrics bridges the gap between complex analytical data and practical decision-making. It’s not just about crunching numbers- it’s about empowering researchers to ensure the safety, quality, and effectiveness of medicines with greater confidence and efficiency. Chemometrics ; Process analytical technology ; Raw data ; Cheminformatics ; Process (computing) Spectroscopy and Chemometric Analyses ; Analytical Methods in Pharmaceuticals ; Pharmaceutical Quality and Counterfeiting
Authors: Ankesh Kumar | Himanshu Khatri | Raghu Solanki | Pankaj Yadav | Dhiraj Bhatia | Amit K. Yadav
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2025-10-29)
Field: Materials Science — Materials Chemistry

Protocol for green synthesis of excellent fluorescent mango leaves-derived carbon for advanced theragnostic in biological applications ABSTRACT Carbon quantum dots (CQDs) are zero-dimensional fluorescence nanoparticles that are less than 10 nm in size. They have unique properties like tunable surface functionality, biocompatibility, fluorescence, and low toxicity. This study outlines a detailed protocol for synthesizing CQDs from mango leaf powder using a reflux method at 160 C and 400 rpm for a duration of two hours. The synthesized CQDs were comprehensively characterized using the following techniques, including UV-visible spectroscopy, fluorescent spectrophotometry, atomic force microscopy, dynamic light scattering and zeta potential measurement, Fourier transform infrared spectroscopy, and scanning electron microscopy. In vitro analysis was conducted through an MTT assay, revealing that the CQDs exhibit reduced toxicity in RPE1 cells compared to the control group. Additionally, a concentration-dependent cellular uptake assay was performed to further evaluate the biological performance of the CQDs. The findings suggest the potential of CQDs as safe and effective nanomaterials for various biological applications, including drug delivery and bioimaging. Fluorescence ; Nanomaterials ; Zeta potential ; Fourier transform infrared spectroscopy ; Dynamic light scattering ; Carbon fibers ; Nanoparticle ; In vitro ; Biological imaging ; Carbon quantum dots Carbon and Quantum Dots Applications ; Advanced Nanomaterials in Catalysis ; Graphene and Nanomaterials Applications
Authors: Júlia Couto | Jamile Raquel Regazzo | Murilo Mesquita Baesso | Adriano Rogério Bruno Tech | Thiago M. D. Silva
Source: Engenharia Agrícola (article, 2025-01-01)
Field: Chemistry — Analytical Chemistry

CLASSIFICATION OF THE NUTRITIONAL CONDITION OF BEAN PLANTS (Phaseolus Vulgaris) USING CONVOLUTIONAL NEURAL NETWORKS AND IMAGE ANALYSIS ABSTRACT Agriculture plays an essential role in Brazil, especially in the production of beans (Phaseolus vulgaris), an important source of plant protein. In this study, a convolutional neural network (CNN) model was developed to classify the nutritional status of the bean plant focusing on nitrogen (N) content, using RGB images. The experiment was conducted at USP, in Pirassununga, with five nitrogen fertilization treatments and 30 bean plant pots. Weekly images of the leaves were captured starting from 30 days after emergence (DAE). The images were processed and used to train and test different CNN configurations. The results indicated that larger sets of images and smaller blocks (10x10 pixels) increased accuracy, especially at 37 DAE. It is concluded that the proposed model is effective for nutritional monitoring, providing an efficient alternative to traditional leaf analysis. Phaseolus ; Convolutional neural network ; Image (mathematics) ; Artificial intelligence ; Computer science ; Pattern recognition (psychology) ; Agronomy ; Biology Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Leaf Properties and Growth Measurement
Authors: Orlando Salazar Campos | Daniel Tafur-Vera | Percimil Lecca-Pino | Javier Moran-Ruiz | Johonathan Salazar Campos
Source: Research Square (preprint, 2025-12-10)
Field: Chemistry — Analytical Chemistry

Post-harvest quality control of white quinoa on smartphones using deep learning Convolutional neural network ; Artificial neural network ; Probabilistic logic ; Inference ; Deep learning ; Generalization Spectroscopy and Chemometric Analyses ; Biosensors and Analytical Detection ; Smart Agriculture and AI
Authors: Lukáš Maňas | Erika Pavlíková | Miroslav Mrlík | Roman Kolařík | Jozef Martinka | Peter Rantuch | Tomáš Sedláček | Tomáš Plachý
Source: Journal of Science Advanced Materials and Devices (article, 2025-09-01)
Field: Materials Science — Polymers and Plastics

Sustainable composite from furfuryl alcohol and wood flour with outstanding fire resistance and its prediction using neural networks Novel composites were successfully produced using renewable green sources furfuryl alcohol resin, commonly obtained from a biomass, and wood flour. Compared with a conventional melt-blending technique used for preparation of wood-plastic composites, this unique approach utilizing low-viscosity thermoset resin with high affinity for wood enables to avoid use of excessive treatment of wood flour. Four flame retardants possessing different flame-retardant mechanisms (expandable graphite (EG), ammonium dihydrogen phosphate (ADP), Exolit OP560 and dimethyl propane phosphonate) at two loading levels (7.5 and 15 wt%) were used to suppress flammability of the composites evaluated by a cone calorimeter test, limiting oxygen index and UL 94. All investigated flame retardants significantly reduced maximum value of heat release rate (HRR) (EG and ADP approx. up to 75 %) and, moreover, ADP and EG significantly reduced the total smoke production (EG up to 25 % and ADP up to 96 %) confirming outstanding and unusual flammability suppression considering HRR reduction and a decrease in smoke production rate (SPR) at the same time. Besides that, the neural network prediction models for HRR and SPR from test time and mass loss rate were created and trained giving the possibility to predict HRR and SPR values from simple and cheap tests providing only mass loss rate at specific conditions. Furfuryl alcohol ; Materials science ; Fire resistance ; Composite number ; Composite material ; Artificial neural network ; Resistance (ecology) ; Alcohol ; Organic chemistry ; Artificial intelligence ; Computer science ; Catalysis Natural Fiber Reinforced Composites ; Flame retardant materials and properties ; Fire dynamics and safety research
Authors: Riki Muhammad | Isroni | Tri Pujian wisesa | Tri Siswandi  Syahputra
Source: Journal of Energy Material and Instrumentation Technology (article, 2025-08-31)
Field: Chemistry — Analytical Chemistry

Development of a Portable Low-Cost Multispectral Sensor Integrated with IoT and Machine Learning for Classifying Honey Types Accurate honey type authentication is a significant challenge for small-scale producers, as conventional methods are often costly and impractical. This study aims to design and develop a low-cost honey classification prototype by integrating the AS7265X multispectral sensor with Internet of Things (IoT) technology and machine learning. Spectral data from 18 channels of various Indonesian honey types were acquired using the AS7265X sensor and analyzed exploratively using Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA). The data were then normalized and used to train Artificial Neural Network (ANN), Random Forest (RF), and Support Vector Machine (SVM) classification models. An ESP32-based IoT system was developed for real-time monitoring and cloud data storage. The results demonstrate that AS7265X spectral data effectively differentiate honey types, with the ANN model achieving 94.05% accuracy, supported by a responsive IoT system (1–2 seconds) for monitoring and centralized storage. This prototype shows potential as a practical, rapid, accurate, and efficient honey authentication solution for various stakeholders. Multispectral image ; Linear discriminant analysis ; Principal component analysis ; Internet of Things ; Support vector machine ; Authentication (law) ; Artificial neural network ; Pattern recognition (psychology) Spectroscopy and Chemometric Analyses ; Bee Products Chemical Analysis ; Advanced Chemical Sensor Technologies
Authors: Berre Kumuk | Nisa Nur Atak | Battal Dogan | Murat Kadir Yesilyurt
Source: Scientia Iranica (article, 2025-11-05)
Field: Materials Science — Materials Chemistry

A Comprehensive Investigation on the Enhancing the Thermal Efficiency of Solid Oxide Fuel Cells through Temperature and Pore Diameter Optimization This study aims to comprehensively analyze the performance of solid oxide fuel cells (SOFCs) operating at high temperatures and converting chemical energy directly into electrical energy, considering the effects of multiple parameters. The cell performance was evaluated in the analysis performed on a cell with an active surface area of ​​0.01 m², a temperature range of 573–1673 K and pore diameters of 3–15 µm. The performance evaluation involved a meticulous examination of activation, ohmic, and concentration losses, along with the determination of cell potential, power density, and thermal efficiency through theoretical analyses. The findings showed that the temperature increase positively affected the cell efficiency up to a certain threshold; it was determined that the thermal efficiency reached its peak especially in the temperature range of 1073 - 1273 K. A 20% efficiency increase was achieved under the conditions of a temperature increase of 210 K, a current density of 10000 A/m² and a pore diameter of 8 µm. While 33.04% and 21.41% efficiency values ​​were obtained with 5 µm and 10 µm pore diameters, respectively, at a constant temperature of 873 K, a 60% increase in pore diameter provided only an 8% efficiency increase, which revealed that the increase in pore diameter had limited and negative effects on efficiency. Unlike the generally single parameter focused cases in the literature, this research demonstrates conclusively that the length of the width diameter will lead to a noticeable decrease in thermal efficiency by providing a joint analysis of multiple variables in the cell. Oxide ; Energy conversion efficiency ; Thermal ; Range (aeronautics) ; Atmospheric temperature range ; Thermal efficiency ; Power density ; Efficient energy use Advancements in Solid Oxide Fuel Cells ; Fuel Cells and Related Materials ; Chemical Looping and Thermochemical Processes
Authors: F. Hafna Ahmed | Lygie Esquirol | Santana Royan | Mitchell M. Birgan | Nigel G. French | Sophia Newton | Alessandro T. Caputo | Colin Scott
Source: Protein Science (article, 2025-12-22)
Field: Materials Science — Biomaterials

Disordered N‐termini enhance the thermostability of <scp>SGNH</scp> ‐hydrolase family polyesterases Polyesters are widely used plastics that persist in the environment due to their resistance to degradation. Microbial polyesterases potentially offer recycling and remediation solutions. However, most polyesterases lack the thermostability and catalytic efficiency required for practical application. Here, we have identified and characterized thermostable bacterial polyesterases from an undercharacterized subfamily of SGNH-hydrolases (related to PpEST from Pseudomonas oleovorans) and uncovered a previously unreported thermal stabilization mechanism mediated by conformationally flexible N-terminal regions with features of intrinsically disordered regions. Biochemical assays, structural analysis, small angle X-ray scattering, X-ray crystallography, and molecular dynamics simulations suggest that these flexible N-terminal regions enhance thermal resilience without affecting the catalytic rate or oligomerization in some homologs, while they promote oligomerization and reduce k<sub>cat</sub> in others. These findings suggest that flexible terminal regions can act as modular stabilizing elements through diverse mechanisms. Our work provides mechanistic insight into an unusual route to protein thermostability, expanding strategies for enzyme engineering, and contributing to the development of robust biocatalysts for polyester degradation. Thermostability ; Polyester ; Catalytic efficiency ; Molecular dynamics ; Subfamily ; Catalysis ; Resilience (materials science) ; Protein structure biodegradable polymer synthesis and properties ; Microplastics and Plastic Pollution ; Enzyme Catalysis and Immobilization
Authors: AM Sule | Paul Bako Yacim | Augustine Ohiole | Fatogun Oluwayomi Patrick
Source: Asian Journal of Chemical Sciences (article, 2025-08-07)
Field: Chemistry — Organic Chemistry

QSAR-based Design of Schiff Base Inhibitors for Drug-resistant Salmonella typhi Salmonella typhi, a Gram-negative pathogen linked to typhoid disease, has shown concerning patterns of antibiotic resistance, highlighting the need for novel inhibitors. By using predicted Quantitative Structure-Activity Relationship (QSAR) models, this study aimed to identify the structural factors present in Schiff bases that have anti-Salmonella typhi activity. After a thorough collection of 43 Schiff bases was compiled, the minimum inhibitory concentrations (MIC) of each were transformed into pMIC values for analytical use. Molecular descriptors were obtained, and QSAR models were constructed using Genetic Function Approximation (GFA). Model 1 emerged as the most robust iteration, with validation metrics (R2 = 0.800, R2adj = 0.749, Q2 = 0.520, R2 - Q2 = 0.280, and R2pred = 0.642) reflecting substantial predictive capability. Model 1 identified Weta3.unity, a molecular weight descriptor, as the dominant descriptor that influences the anti-Salmonella typhi activity of Schiff bases. The findings highlight how molecular weight affects the efficacy of anti-Salmonella typhi, laying the groundwork for the logical development of more potent Schiff base derivatives. Salmonella typhi ; Quantitative structure–activity relationship ; Typhoid fever ; Salmonella ; Schiff base ; Antibiotics ; Chemistry ; Computational biology ; Microbiology ; Biology ; Stereochemistry ; Bacteria ; Biochemistry ; Escherichia coli ; Genetics ; Gene Synthesis and biological activity ; Computational Drug Discovery Methods ; Synthesis and Characterization of Heterocyclic Compounds
Authors: Seojin Kim | Houbing Song | Jae Hyun Nam | Jinwoo Shin
Source:  (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Training Text-to-Molecule Models with Context-Aware Tokenization Recently, text-to-molecule models have shown great potential across various chemical applications, e.g., drug-discovery.These models adapt language models to molecular data by representing molecules as sequences of atoms.However, they rely on atom-level tokenizations, which primarily focus on modeling local connectivity, thereby limiting the ability of models to capture the global structural context within molecules.To tackle this issue, we propose a novel text-to-molecule model, coined Context-Aware Molecular T5 (CAMT5).Inspired by the significance of the substructure-level contexts in understanding molecule structures, e.g., ring systems, we introduce substructurelevel tokenization for text-to-molecule models.Building on our tokenization scheme, we develop an importance-based training strategy that prioritizes key substructures, enabling CAMT5 to better capture the molecular semantics.Extensive experiments verify the superiority of CAMT5 in various text-to-molecule generation tasks.Intriguingly, we find that CAMT5 outperforms the state-of-the-art methods using only 2% of training tokens.In addition, we propose a simple yet effective ensemble strategy that aggregates the outputs of textto-molecule models to further boost the generation performance.Code is available at https: //github.com/Songhyeontae/CAMT5.git. Training (meteorology) ; Training set ; Lexical analysis ; Feature (linguistics) Machine Learning in Materials Science ; DNA and Biological Computing ; Graph Theory and Algorithms