jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Yan Song Jiang | Yihang Chen | Yufei Cai | Kaixuan Zhou | Hayden J. Ousley | Jun Li | H. Tom Soh | Kaiyu Fu
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2025-09-30)
Field: Chemistry — Electrochemistry

Algorithm-Powered Analyzer for Continuous Electrochemistry: A Toolkit for Real-Time Electrochemical Data Analysis ABSTRACT Real-time biosensors offer significant potential for continuous monitoring of biomolecules. However, their practical application and further development face challenges on data analysis, including poor signal-to-noise ratio when effective sensing area decreases due to signal degradation by biofouling, time-consuming and subjective process of manual or semi-automated peak identification, and inconsistencies in data interpretation, thereby complicating reproducibility and cross-comparison of biosensing results. In this study, we introduce the Algorithm-Powered Analyzer for Continuous Electrochemistry (A-PACE), an open-source toolkit providing streamlined and automated data analysis protocol optimized for real-time electrochemical data analysis. A-PACE comprises three modules: (1) Change point detection for automated identification of peak regions; (2) Baseline fitting with multiple algorithms to handle diverse electrochemical signals and baseline screening to eliminate unreasonable fits; (3) Large dataset input with user-friendly interface for data processing, exporting and visualization. To ensure optimal performance, we curated a training set of 2000 electrochemical curves from an extensive electrochemical dataset (&gt;100,000 curves) collected over the past five years under varied electrochemical measurement conditions. These curves were analyzed using 2046 algorithm sets to identify a default algorithm set, compared to existing tools, demonstrating its capability in peak detection and baseline fitting across a broad spectrum of electrochemical data. Case studies reveal the A-PACE can analyze month-long in vitro serum data and week-long in vivo intravenous blood data, extending the operational lifespan of real-time sensors. This cross-platform compatible toolkit supports both real-time and post-processing analysis, reducing the processing time from minute level per signal by human labeling to second level by A-PACE and subjectivity associated with electrochemical signal processing. By providing this solution for continuous electrochemical data analysis, A-PACE enhances biosensors’ applications in medical diagnostics and continuous monitoring with high throughput analysis. Spectrum analyzer ; Process (computing) ; SIGNAL (programming language) ; Identification (biology) ; Reproducibility ; Data reduction ; Set (abstract data type) ; Protocol (science) ; Interface (matter) Electrochemical Analysis and Applications ; Advanced Chemical Sensor Technologies ; Electrochemical sensors and biosensors
Authors: Guolong Jin | Jingrui Liang | Lei Zhang | Haoqing Xu | Haoran Li | Shengwei Wang
Source: Buildings (article, 2025-08-22)
Field: Materials Science — Materials Chemistry

Study on Influencing Factors of Strength of Plastic Concrete Vertical Cutoff Wall Vertical containment barriers—critical for intercepting contaminant transport in subsurface environments—demand materials that balance low permeability with adequate strength, particularly in stress-sensitive mountainous terrain. Plastic concrete, as a key barrier material, provides essential properties, including exceptional stress relaxation, to suppress fracture development under compressive loads, coupled with effective seepage control. This study examines its strength performance through experiments on varied mixing techniques (dry, wet, and 24 h hydration), unconfined compression under uncontaminated conditions (water–binder ratios: 1.3–2.1, bentonite content: 20–30%, ages: 14–90 days), barium ion immersion (1–5 g/L, pH 7–11), and dry–wet cycling (10 cycles). Key findings demonstrate that (1) the strength of samples prepared by dry mixing and wet mixing is lower than that of samples mixed for 24 h, and all specimens met the target design strength following 28 days of curing; (2) under pollution-free conditions, strength decreases with higher water–binder ratios and bentonite content, showing a linear relationship. Strength increases exponentially with age; (3) in the presence of Ba2+, strength gradually decreases as Ba2+ concentration and pH increase, particularly notably at 3 g/L Ba2+ and pH 11. Strength increases with age, following a power relationship; (4) under dry–wet cycles, ion concentration has minimal impact on sample quality and surface state but significantly affects strength, with higher ion concentrations leading to greater strength loss and susceptibility to cycles; (5) during solution immersion, higher ion concentrations and pHs result in greater strength loss and worse erosion resistance. Cutoff ; Materials science ; Structural engineering ; Geotechnical engineering ; Composite material ; Engineering ; Physics Magnesium Oxide Properties and Applications ; Concrete and Cement Materials Research ; Grouting, Rheology, and Soil Mechanics
Authors: Irin Wanthong | Theeraphat Sri-on | Somboonsub Rodporn | Siripong Pawako | Sorada Khaengkarn | Jiraphon Srisertpol
Source: Preprints.org (preprint, 2025-09-19)
Field: Chemistry — Analytical Chemistry

A Computer Vision and AI-Based System for Real-Time Sizing and Grading of Thai Export Fruits Thailand’s mango export industry faces significant challenges in meeting stringent international quality standards, particularly the costly phytosanitary X-ray irradiation process. Current fixed-dose irradiation methods result in substantial energy waste due to variations in fruit size. This research presents a low-cost, real-time system that integrates computer vision and artificial intelligence (AI) to optimize this process. By capturing a single top-view 2D image, the system accurately estimates the three-dimensional characteristics (width, height, and depth) of &amp;#039;Nam Dok Mai Si Thong&amp;#039; mangoes. This dimensional data is crucial for dynamically adjusting the radiation dose for each fruit, leading to significant reductions in energy consumption and operational costs. Our novel approach utilizes a Linear Regression combined with Co-Kriging (LR+CoK) model to precisely estimate fruit depth from 2D data, a key limitation in previous studies. The system demonstrated high efficacy, achieving a dimensional estimation error (RMSE) of less than 0.46 cm and a size grading accuracy of up to 93.33 percent. This technology not only enhances sorting and grading efficiency but also offers a practical solution to lower the economic and energy burden of phytosanitary treatments, directly improving the sustainability of fruit export operations. Sizing ; Grading (engineering) ; Energy consumption ; Phytosanitary certification ; Machine vision ; Automation ; Sustainability ; Regression analysis Spectroscopy and Chemometric Analyses
Authors: Ken-ichi Hirano | Hiroki Okudera
Source: Zeitschrift für Kristallographie - Crystalline Materials (article, 2025-04-29)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Lead phosphate oxyapatite, Pb<sub>10</sub>(PO<sub>4</sub>)<sub>6</sub>O, with <i>c</i>-double superstructure Abstract Single crystal specimens of lead phosphate oxyapatite, Pb 10 (PO 4 ) 6 O, were prepared and the structure was refined in <m:math xmlns:m="http://www.w3.org/1998/Math/MathML"> <m:mrow> <m:mi>P</m:mi> <m:mover accent="true"> <m:mn>6</m:mn> <m:mo>‾</m:mo> </m:mover> </m:mrow> </m:math> $P\overline{6}$ space group. The crystal structure was a superstructure of aristotype apatite structure doubled in the c -axis. This superstructure was characterized by change in width of the anion channel, namely change in sizes of regular A 2 triangles as ‘large – middle – small – middle – (large – )’ along &lt;001&gt; in 2 × c of the parent structure. A half of in-channel (guest) O 2− were located at either one of two positions ( X 1 site positions) split from the center of the smallest triangle ( A 2c triangle) along &lt;001&gt; with 50 % probability each. The rest scattered over four positions on the c -axis near two middle-size triangles in the cell with far less probabilities. Attraction between A 2c-site Pb 2+ and X 1-site O 2− made A 2c triangle the smallest, while 6s2 lone electron pairs of Pb 2+ hindered O 2− to locate at the center of A 2c triangle. This commensurately modulated structure was required to balance different sizes of A 2 triangles and least distortion of (PO 4 ) 3− complex anion simultaneously in the host structure. Superstructure ; Lead (geology) ; Materials science ; Double layered ; Phosphate ; Analytical Chemistry (journal) ; Mineralogy ; Chemistry ; Physics ; Geology ; Environmental chemistry ; Thermodynamics ; Composite material Crystal Structures and Properties ; Solid-state spectroscopy and crystallography ; Inorganic Chemistry and Materials
Authors: Achintya Singha | Mamta Gokhale | Hemanga Kumar Das | Rumana Faraz | Arushi Garg
Source: International Journal of Plant and Environment (article, 2025-09-30)
Field: Materials Science — Materials Chemistry

Exploring the Applications and Implications of Silver Nitrate and Silver Nanoparticles in Plant Tissue Culture Silver nitrate and silver nanoparticles have emerged as versatile tools in plant tissue culture, offering a range of applications in micropropagation, regeneration, and metabolite production. This review highlights the benefits and limitations of using silver nitrate and silver nanoparticles in plant tissue culture, including their role in enhancing shoot growth, regeneration, and secondary metabolite production. Additionally, their potential in large-scale propagation, preservation of ornamental plants, and salinity tolerance is discussed. While silver nitrate and silver nanoparticles have shown promise, their use also raises concerns about phytotoxicity and genetic instability. This review aims to provide a comprehensive understanding of the applications and implications of using silver nitrate and silver nanoparticles in plant tissue culture, shedding light on their potential for future research and applications. Silver nanoparticle ; Silver nitrate ; Nitrate ; Phytotoxicity ; Plant tissue ; Plant tissue culture ; Nanoparticle Nanoparticles: synthesis and applications ; Plant tissue culture and regeneration ; Plant Stress Responses and Tolerance
Authors: Hamid A A. Khafaji | Ghada Majid Al-Ghanimi
Source: International Innovations Journal of Applied Science (article, 2025-09-15)
Field: Materials Science — Biomaterials

Evaluation of the effectiveness of chitosan and Threonine in protecting tomato plants from root and stem rot disease caused by the fungus Fusarium solani The aim of the study was to protect tomato plants from root and stem rot disease caused by the fungus Fusarium solani under test using regular chitosan and threonine (Thr) at concentrations of 0.5, 1.0, 1.5, and 2.0 g L-1 for each. This was done by measuring the intensity of certain growth parameters and peroxidase activity. Compared with the control treatment, the results showed that all plant treatments, including chitosan and threonine, protected tomato plants by inducing systemic resistance to root and stem rot disease. The plant defense mechanisms were activated as evidenced by increased peroxidase activity, which typically results in growth parameters such as increased length and weight of both fresh and dry vegetable groups with statistically significant differences compared to the pathogenic fungus treatment. Root rot ; Fungus ; Peroxidase ; Fusarium ; Fusarium solani ; Pathogenic fungus ; Stem rot Nanocomposite Films for Food Packaging ; Plant Growth Enhancement Techniques ; Agriculture, Plant Science, Crop Management
Authors: Marco Ferrari
Source: American Journal Of Chemistry And Chemical Engineering (article, 2025-12-31)
Field: Materials Science — Biomaterials

Advances in Chemical Engineering for Food Processing and Preservation The food industry is constantly evolving to meet the demands for higher quality, safety, and longer shelf life of food products. Chemical engineering plays a significant role in developing advanced techniques and processes for food processing and preservation, ensuring that food products are safe, nutritious, and sustainable. This article explores the latest advances in chemical engineering that have improved food processing methods, including novel preservation techniques, packaging innovations, and energy-efficient processes. It discusses the challenges of scaling up laboratory innovations, sustainability in food production, and the future directions of food processing in chemical engineering Food processing ; Food engineering ; Food industry ; Sustainability ; Chemical industry ; Food technology ; Food packaging Nanocomposite Films for Food Packaging ; Food Drying and Modeling ; Microencapsulation and Drying Processes
Authors: Mingfei Sun | Yang Zhan | Ziwei Ma | Jinbo Wu | Shaoying Lu
Source: Frontiers in Bioengineering and Biotechnology (article, 2025-05-15)
Field: Materials Science — Materials Chemistry

A ultra-stable point of care nanozyme-based kit for cTnI detection in human serum Home discovery of myocardial infarction can significantly improve the rate of treatment. Cardiac troponin I (cTnI), as a biochemical marker that has been introduced into clinical diagnosis and treatment guidelines, can effectively predict the occurrence of myocardial infarction. We constructed highly stable nanozyme based on Fe<sub>3</sub>O<sub>4</sub> nanoparticles and prepared rapid detection reagents for myocardial infarction by modifying anti-cTnI antibodies. The results showed that the nanozyme with an average particle size of 200 nm had peroxidase activity and could effectively catalyze 3,3', 5,5'-tetramethylbenzidine (TMB). With a sensitivity of up to 1.5 ng/mL, the kit showed superior performance than commercial colloidal gold assay kits, and could effectively detect cTnI in serum with an overall compliance rate of 92.1%. The study provides a new approach to home detection of heart attacks. Troponin I ; Point-of-care testing ; Point of care ; Medicine ; Internal medicine ; Pathology Advanced Nanomaterials in Catalysis ; Biosensors and Analytical Detection ; Retinal and Optic Conditions
Authors: Naresh Pala Naresh Pala
Source: International Journal of Advances in Engineering and Management (article, 2025-04-01)
Field: Chemistry — Analytical Chemistry

Automation in Grocery Fulfillment Centers: Transforming Efficiency and Customer Experience Automation in grocery fulfillment centers represents a transformative force in retail, revolutionizing how customer orders are processed, picked, packed, and dispatched. This technological revolution leverages robotics, artificial intelligence, and data analytics to address critical industry challenges, including perishability concerns, temperature control requirements, and complex inventory management. Integrating automated storage and retrieval systems, goods-to-person technologies, autonomous mobile robots, and robotic picking arms significantly enhances operational efficiency. At the same time, artificial intelligence serves as the decision-making engine across multiple domains. Innovative solutions are emerging despite implementation challenges, including substantial capital requirements, legacy system integration complexities, workforce transition needs, and balancing standardization with flexibility. The measurable benefits span enhanced order accuracy, labor efficiency transformation, accelerated processing, inventory optimization, last-mile efficiency, and waste reduction. The Kroger-Ocado partnership demonstrates these principles in action. At the same time, emerging technologies, including micro-fulfillment centers, quantum computing applications, advanced materials handling, and sustainable automation practices, point toward future evolution in the industry. Automation ; Business ; Marketing ; Customer experience ; Computer science ; Process management ; Manufacturing engineering ; Engineering ; Mechanical engineering Spectroscopy and Chemometric Analyses ; Food Supply Chain Traceability ; Smart Agriculture and AI
Authors: Youssef Ouballouk | Abdelkader Chahlaoui | Chouaib Amahzoune | Abdelilah Rahou | Abdelhak Saidi
Source: Research Square (preprint, 2025-09-09)
Field: Materials Science — Biomaterials

Diatom Diversity in the Surface Waters of Oued Beht Diatom ; Bioindicator ; Navicula ; Ecosystem ; Water quality ; Aquatic ecosystem ; Salinity ; Limnology Diatoms and Algae Research ; Freshwater macroinvertebrate diversity and ecology ; Aquatic Invertebrate Ecology and Behavior
Authors: Mutasar Mutasar | Chaeroen Niesa
Source: JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) (article, 2025-02-12)
Field: Chemistry — Analytical Chemistry

COMBINATION OF LEARNING VECTOR QUANTIZATION AND LINEAR DISCRIMINANT ANALYSIS FOR TEA LEAF DISEASE CLASSIFICATION Tea farming, one of the key pillars of Indonesia's economy, faces productivity challenges due to diseases affecting tea leaves. Manual identification of tea leaf diseases requires significant time and cost, making an automated solution necessary. This research develops an innovative model for classifying tea leaf diseases by synergizing Learning Vector Quantization (LVQ) and Linear Discriminant Analysis (LDA). By leveraging LVQ’s prototype-based classification and LDA’s dimensionality reduction, the model ensures accurate and efficient disease identification. During preprocessing, tea leaf images were converted to the CIELAB color space to enhance segmentation using Otsu’s Thresholding. Features such as Mean Color and texture attributes based on Gray Level Co-occurrence Matrix (GLCM) were extracted, reduced via LDA, and classified using LVQ. Tested on five tea leaf disease classes, the model achieved 94.1% accuracy. This performance underscores its potential to significantly assist farmers in early detection and management of tea leaf diseases, while also providing researchers with a robust tool for advancing agricultural technology. Linear discriminant analysis ; Learning vector quantization ; Pattern recognition (psychology) ; Artificial intelligence ; Mathematics ; Vector quantization ; Machine learning ; Statistics ; Computer science Spectroscopy and Chemometric Analyses ; Leaf Properties and Growth Measurement ; Smart Agriculture and AI
Authors: Katelyn Lazareno | Chris Chae | Becky Haight | Shams Jabin | Rachel Steinhardt | John J. Plombon | Siddharth Rajan | Patrick M. Woodward | Jinwoo Hwang | Fengyuan Yang
Source: ArXiv.org (preprint, 2025-10-20)
Field: Materials Science — Materials Chemistry

Broad-Range Tuning of Ferroelectric Switching of LaxBi1-xFeO3 Epitaxial Films via Digital Doping using Off-Axis Co-Sputtering To investigate the scope of ferroelectric behavior in La-substituted BiFeO3 films, LaxBi1-xFeO3 epitaxial films were synthesized using off-axis co-sputtering on SrTiO3(001) and DyScO3(110) substrates with a SrRuO3 bottom electrode layer. A digital-doping deposition method was used to enable precise control and continuous tuning of La concentration in high-quality LaxBi1-xFeO3 films across a wide range of x = 0.05-0.60, which was systematically investigated using piezoresponse force microscopy. Robust and reversible out-of-plane ferroelectric switching has been observed up to x = 0.35, while films with x $\geq$ 0.37 exhibit no measurable ferroelectric behavior, indicating a sharp ferroelectric-to-paraelectric phase transition between x = 0.35 and 0.37. This represents the highest reported La concentration in LaxBi1-xFeO3 films that retains ferroelectric ordering, highlighting opportunities to engineer ferroelectric and multiferroic properties in complex oxide heterostructures. Ferroelectricity ; Piezoresponse force microscopy ; Multiferroics ; Epitaxy ; Electrode ; Doping ; Thin film ; Deposition (geology) Ferroelectric and Piezoelectric Materials ; Multiferroics and related materials ; Ferroelectric and Negative Capacitance Devices
Authors: Wanisa Promdee | Sukho Kongtawong | Surakawin Suebka | Thapakron Pulampong | Natthawut Suradet | Roengrut Rujanakraikarn | Puttimate Hirunuran | Siriwan Jummunt
Source: Particles (article, 2025-08-25)
Field: Chemistry — Spectroscopy

The Study and Development of BPM Noise Monitoring at the Siam Photon Source This study presents the development of a noise-monitoring system for the storage ring at the Siam Photon Source, designed to detect and classify noise patterns in real time using beam position monitor (BPM) data. Noise patterns were categorized into four classes: broad peak, multipeak, normal peak, and no beam. Two BPMs located at the multipole wiggler section, BPM-MPW1 and BPM-MPW2, were selected for detailed monitoring based on consistent noise trends observed across the ring. The dataset was organized in two complementary formats: two-dimensional (2D) images used for training and validating the models and one-dimensional (1D) CSV files containing the corresponding raw numerical signal data. Pre-trained deep learning and 1D convolutional neural network (CNN) models were employed to classify these patterns, achieving an overall classification accuracy of up to 99.83%. The system integrates with the EPICS control framework and archiver log data, enabling continuous data acquisition and long-term analyses. Visualization and monitoring features were developed using CS-Studio/Phoebus, providing both operators and beamline scientists with intuitive tools to track beam quality and investigate noise-related anomalies. This approach highlights the potential of combining beam diagnostics with machine learning to enhance operational stability and optimize the synchrotron radiation performance for user experiments. Noise (video) ; Photon ; Light source ; Computer science ; Acoustics ; Physics ; Optics ; Artificial intelligence Spectroscopy and Laser Applications ; Laser Design and Applications ; Atomic and Subatomic Physics Research
Authors: Shahzodbek E. Rakhimjonov | A. A. Umarov
Source: International Journal of Advance Scientific Research (article, 2025-06-01)
Field: Materials Science — Polymers and Plastics

Theoretical Study of Structural Defects in Textile Fabrics This article broadly covers modern research directions for identifying and evaluating structural defects in textile fabrics. These defects significantly affect the quality, aesthetic appearance, and functional properties of fabrics. Therefore, detecting, diagnosing, and effectively eliminating them is one of the most pressing issues in the textile industry. The study explores advanced techniques for identifying structural defects using image processing algorithms, deep learning technologies, and neural networks. It examines the potential of a deep learning approach based on the Fisher criterion to ensure high accuracy in fabric quality diagnostics. New approaches using Gabor filters to analyze the spectral and geometric parameters of fabrics are also discussed. Methods aimed at improving the automated detection of defect shape, location, and density on fabric surfaces are presented. The research results include practical recommendations to optimize production processes and enhance quality control. In particular, it outlines opportunities to take significant steps in defect detection and prevention through the implementation of modern technologies. This study not only contributes to improving fabric quality but also lays a vital scientific foundation for the sustainable development of the textile industry. Textile ; Materials science ; Composite material Textile materials and evaluations