jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Mehrab Mahdian | Ferenc Ender | Tamás Párdy
Source:  (preprint, 2025-06-26)
Field: Materials Science — Biomaterials

An Open Web Platform for Standardized, Community-Driven Electrospinning Data Collection Electrospinning is a foundational technique for producing nanofibers, but progress is hindered by fragmented, inconsistent, and non-reproducible experimental data. We present an open-source, FAIR-compliant web platform for standardized collection, cur Electrospinning ; Data collection ; World Wide Web ; Computer science ; Business ; Chemistry ; Mathematics ; Statistics Electrospun Nanofibers in Biomedical Applications
Authors: Sheng-Yang Huang | Hsien-Yü Hsü | A. Mohamed Musthafa | I-An Lin | Chia-Man Chou | Vincent K. S. Hsiao
Source: Micromachines (article, 2025-10-22)
Field: Materials Science — Materials Chemistry

Tuning Multi-Wavelength Reflection Properties of Porous Silicon Bragg Reflectors Using Silver-Nanoparticle-Assisted Electrochemical Etching This study proposes an innovative silver-nanoparticle-assisted electrochemical etching method for the fabrication of porous silicon Bragg reflectors with multi-wavelength reflection characteristics. By introducing silver nanoparticles at varying concentrations (0.1-10 mg/mL) into the conventional HF-ethanol electrolyte and applying periodically modulated current densities (40/100 mA/cm<sup>2</sup>), the transition from single-peak to multi-peak reflection spectra was successfully achieved. The results demonstrate that at a concentration of 10 mg/mL silver nanoparticles, up to four distinct reflection bands can be obtained. A systematic investigation was conducted on the influence of etching cycles (4-20 cycles) and silver nanoparticle concentration on the optical performance and microstructure. SEM analysis revealed well-defined periodic multilayer structures, while XPS analysis confirmed the presence of metallic silver on the porous silicon surface. This work provides a simple, controllable, and cost-effective approach to the development of multifunctional photonic devices, with promising applications in laser optics, solar cells, chemical sensing, and surface-enhanced Raman scattering. Porous silicon ; Etching (microfabrication) ; Reflection (computer programming) ; Silicon ; Raman spectroscopy ; Fabrication ; Electrolyte ; Silver nanoparticle Silicon Nanostructures and Photoluminescence ; Nanowire Synthesis and Applications ; Photonic Crystals and Applications
Authors: Dongfang Zhang | Shuangxia Luo | Jun Zhang | Mingxuan Li | Xiaofei Fan | Xueping Chen | Shuxing Shen
Source: Agronomy (article, 2025-07-25)
Field: Chemistry — Analytical Chemistry

Multispectral and Chlorophyll Fluorescence Imaging Fusion Using 2D-CNN and Transfer Learning for Cross-Cultivar Early Detection of Verticillium Wilt in Eggplants Verticillium wilt is characterized by chlorosis in leaves and is a devastating disease in eggplant. Early diagnosis, prior to the manifestation of symptoms, enables targeted management of the disease. In this study, we aim to detect early leaf wilt in eggplant leaves caused by Verticillium dahliae by integrating multispectral imaging with machine learning and deep learning techniques. Multispectral and chlorophyll fluorescence images were collected from leaves of the inbred eggplant line 11-435, including data on image texture, spectral reflectance, and chlorophyll fluorescence. Subsequently, we established a multispectral data model, fusion information model, and multispectral image–information fusion model. The multispectral image–information fusion model, integrated with a two-dimensional convolutional neural network (2D-CNN), demonstrated optimal performance in classifying early-stage Verticillium wilt infection, achieving a test accuracy of 99.37%. Additionally, transfer learning enabled us to diagnose early leaf wilt in another eggplant variety, the inbred line 14-345, with an accuracy of 84.54 ± 1.82%. Compared to traditional methods that rely on visible symptom observation and typically require about 10 days to confirm infection, this study achieved early detection of Verticillium wilt as soon as the third day post-inoculation. These findings underscore the potential of the fusion model as a valuable tool for the early detection of pre-symptomatic states in infected plants, thereby offering theoretical support for in-field detection of eggplant health. Verticillium wilt ; Multispectral image ; Verticillium dahliae ; Biology ; Artificial intelligence ; Convolutional neural network ; Computer science ; Botany Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Plant Pathogens and Fungal Diseases
Authors: Divyank Mahajan | Shikha Kaushik | Tapasya Srivastava
Source: Bioscience Reports (review, 2025-10-01)
Field: Chemistry — Spectroscopy

Evolving stratification and biomarker discovery in cancer research with technological advancement of proteomics: 35 years and counting Proteome, the molecular product of regulatory diktat of the cellular machinery, predicts the behaviour and progression of cancers. Designing effective molecular therapies based on proteins with comprehensive patient stratification remains the mainstay of every translational research. Research on the proteome involves a) identification of biomarkers that, with utmost sensitivity and specificity, reveal significant insights into the disease state and b) understanding the mechanistic underpinnings and rewiring of cellular signaling pathways that drive a particular cancerous pathology. In this review, we give a comprehensive description of the evolution of mass spectrometer-based methods, including labeling strategies available to study the proteome and post-translational modifications in response to various perturbations. We summarize their utility in understanding complex processes of cancers, advance research on cancer therapy by decoding novel biomarkers, identify therapy resistance drivers, and enhance spatial attributes of tumor microenvironment by single-cell proteomics. Finally, some of the challenges in the currently used methods have been discussed. Biomarker discovery ; Proteome ; Translational research ; Biomarker ; Identification (biology) ; Disease ; Proteomics ; Cancer Advanced Proteomics Techniques and Applications ; Genetics, Bioinformatics, and Biomedical Research ; Advanced Biosensing Techniques and Applications
Authors: Staphney Texina | Sathees Kumar Nataraj | Alagammai Renganathan | Kavitha Vasantha
Source: Nature Environment and Pollution Technology (article, 2025-06-01)
Field: Chemistry — Analytical Chemistry

Prediction on the Level of Toxicity in Fruits and Vegetables Based on PAHs Using Machine Learning This study focuses on assessing the toxicity levels in fruits and vegetables based on the presence of polycyclic aromatic hydrocarbons (PAHs), particularly in regions affected by industrial and vehicular pollution where the particulate matter deposits on the plant surfaces. Traditional methods, including Gas Chromatography/Mass Spectrometry (GC/MS) and HighPerformance Liquid Chromatography (HPLC), are used to measure PAH levels in fruits and vegetables, which are found to be valuable but expensive and time-consuming. However, the detection of toxicity relies on either expert knowledge or experimental analysis when compared with the limitations set by EFSA (European Food Safety Authority). Therefore, in this study, artificial intelligence techniques have been employed to evaluate the toxicity levels based on 16 PAHs. The PAH concentrations in fruits and vegetables were collected from different articles corresponding to safe and unsafe datasets and then validated through statistical analysis. The validated dataset is classified using different machine learning algorithms. Based on the output from the neural network, the level of toxicity is also scaled and compared with the targeted outputs. The promising results of the classification of toxicity using artificial intelligence methods are substantiated by an experimental study and validated through statistical methods. From the results, it can be observed that the machine learning algorithm has given classification accuracy of more than 90% along with their degree of harmfulness. This research holds implications for food safety and public health, offering a novel approach to the interdisciplinary understanding of climate change by addressing the impact of environmental contaminants on the edibility of fruits and vegetables. Environmental science ; Toxicity ; Environmental chemistry ; Chemistry Spectroscopy and Chemometric Analyses
Authors: Alonkrita Chowdhury | Mayukh Ghosh
Source: Frontiers in Nanotechnology (article, 2025-12-10)
Field: Materials Science — Materials Chemistry

Engineered nanomaterials and the microbiome: assessing disruptions in environmental and human microbial communities The rapid advancement and integration of engineered nanomaterials (ENMs) into consumer products, industrial processes, biomedical applications, and environmental technologies have revolutionized multiple sectors. However, their increased production and environmental release raise critical concerns about unintended interactions with microbial ecosystems. ENMs, including metal-based nanoparticles (silver, titanium dioxide, zinc oxide) and carbon nanomaterials (graphene, carbon nanotubes), possess unique physicochemical properties such as high surface area-to-volume ratios, tunable reactivity, and antimicrobial potential that allow them to interact directly with microbial cells or indirectly influence their habitats. This review critically examines the emerging evidence on ENM–microbiome interactions across human, aquatic, terrestrial, and agricultural systems. In human-associated microbiomes, especially the gut, ENMs can induce dysbiosis by disrupting microbial diversity, altering metabolite production (e.g., short-chain fatty acids), and impairing gut barrier integrity, contributing to inflammation and metabolic disorders. In environmental settings, ENMs influence key microbial functions like nitrogen fixation, organic matter decomposition, and biogeochemical cycling, potentially undermining ecosystem stability and agricultural productivity. Moreover, ENMs are increasingly implicated in accelerating antimicrobial resistance by promoting horizontal gene transfer and enriching resistance genes in microbial communities. The review highlights methodological advances such as high-throughput sequencing, meta-omics approaches, in vitro colon simulators, and in vivo models that have enhanced the assessment of ENM-induced microbiome alterations. Despite these advances, significant gaps remain in understanding long-term and low-dose effects, dose–response relationships, and ecological thresholds. Addressing these gaps through multidisciplinary research and regulatory frameworks is essential for ensuring the safe and sustainable deployment of nanotechnologies in a microbiome-sensitive world. Microbiome ; Dysbiosis ; Metagenomics ; Antibiotic resistance ; Human health ; Unintended consequences Nanoparticles: synthesis and applications ; Microbial Community Ecology and Physiology ; Graphene and Nanomaterials Applications
Authors: Ali Akbari | Fatemeh Ghazi | Yousef Kazemzadeh | A.A. Ranjbar | M. Vatandoust
Source: International Journal of Chemical Engineering (article, 2025-01-01)
Field: Chemistry — Analytical Chemistry

Predicting Asphaltene Stability Using Machine Learning Models Based on SARA Analysis Accurately estimating asphaltene stability in oil and gas operations is critical for ensuring uninterrupted production and preventing flow assurance challenges caused by asphaltene deposition and precipitation. The accumulation of asphaltene can lead to severe operational issues, including reduced well productivity, increased maintenance costs, and potential pipeline blockages. Conventional laboratory methods for evaluating asphaltene stability, while effective, are often time‐intensive and expensive, necessitating the development of alternative predictive approaches. In this study, a reliable and efficient machine learning model was developed to predict the asphaltene weight percentage (wt.%) in crude oil mixtures using SARA (saturates, aromatics, resins, and asphaltenes) data. The initial dataset comprised 1948 samples, from which 21.34% of the data were removed through outlier detection using the Gaussian method, resulting in a final dataset of 1532 samples for model development. To achieve this, 9 ML algorithms were assessed and compared, including artificial neural network (ANN), decision tree (DT), Gaussian process regression (GPR), K‐nearest neighbor (KNN), linear regression (LR), multiple linear regression (MLR), random forest (RF), support vector machine (SVM), and support vector regression (SVR). The performance of these models was evaluated based on their predictive accuracy and error metrics. Among the examined algorithms, KNNs demonstrated the highest predictive capability, achieving an R 2 value of 0.9649 on the test dataset and 0.9636 on the training dataset. Furthermore, the model exhibited a root mean square error (RMSE) of 2.481 for the test data and 2.329 for the training data, indicating its robustness and reliability. The superior performance of KNN suggests that ML can serve as a powerful and cost‐effective alternative to conventional laboratory techniques for asphaltene stability assessment. Support vector machine ; Decision tree ; Artificial neural network ; Random forest ; Asphaltene ; Linear regression ; Outlier ; Regression ; Robustness (evolution) Petroleum Processing and Analysis ; Mineral Processing and Grinding ; NMR spectroscopy and applications
Authors: Farbod Ebrahimi | Farshad Ebrahimi | Anjali Kumari | Saqer Al Abdullah | Kristen Dellinger
Source: Analysis & Sensing (article, 2025-07-21)
Field: Chemistry — Electrochemistry

AI‐Assisted Customizable SERS‐Based Aptasensor for Label‐Free, Rapid, and Sensitive Detection of Tau: Comparative Analysis Using Random Forest and Convolutional Neural Networks Early diagnosis of Alzheimer's disease (AD) is challenging due to the limitations of current biomarker detection methods. A customizable SERS‐based aptasensor platform is presented that combines aptamer‐functionalized gold nanoparticles (AuNPs) with machine learning (ML) algorithms for rapid and sensitive tau protein quantification, a key biomarker for AD. Through systematic evaluation of nanoprobes conjugated with four different tau‐specific aptamer sequences, two aptamer configurations, named AT and BT, are identified as optimal candidates, demonstrating enhancement factors (EFs) of 2.12 × 10 3 and 1.82 × 10 3 , respectively. This approach enables label‐free detection within 30 min and integrates Random Forest (RF) and Convolutional Neural Network (CNN) models for concentration prediction of unknown samples. The RF models achieve remarkable accuracy with R 2 values of 0.998 for AT and 0.9999 for BT configurations, while the CNN models demonstrate strong performance with R 2 values of 0.968 (AT) and 0.986 (BT). The platform achieves a detection limit of 100 pM, well within the clinically relevant ranges. This label‐free approach offers advantages in terms of rapid detection time, portability, and potential adaptability to other biomarkers. The integration of direct SERS sensing with ML algorithms for automated concentration prediction represents a promising advancement in biomarker analysis. Random forest ; Convolutional neural network ; Computer science ; Artificial intelligence ; Pattern recognition (psychology) Electrochemical Analysis and Applications ; Advanced biosensing and bioanalysis techniques ; Electrochemical sensors and biosensors
Authors: Beom Su  Seo | Dong Oh Moon
Source: Cellular and Molecular Biology (article, 2025-12-28)
Field: Chemistry — Analytical Chemistry

Structure-based identification of liensinine as a natural allosteric SHP2 inhibitorwith anti-proliferative activity in HepG2 cells SHP2, encoded by the PTPN11 gene, is a non-receptor tyrosine phosphatase that plays a key role in oncogenic Ras/MAPK signaling. Aberrant SHP2 activity contributes to the progression of various cancers, including liver cancer. In this study, we used an AI-based virtual screening platform (HyperLab) to evaluate 127 natural compounds for SHP2 allosteric inhibition. Liensinine, a bisbenzylisoquinoline alkaloid from Nelumbo nucifera, was identified as a top candidate with strong predicted binding to the SHP2 allosteric tunnel site. This tunnel-shaped pocket is located at the interface between the N-SH2, C-SH2, and PTP domains, where allosteric inhibitors stabilize SHP2 in its closed, inactive conformation by preventing domain rearrangement. Docking analyses using HyperLab and CB-Dock2 consistently supported its interaction with key regulatory residues. Biochemical assays confirmed that Liensinine inhibits SHP2 phosphatase activity in a dose-dependent manner, with an IC₅₀ of ~5.2 μM. In HepG2 cells, Liensinine reduced cell viability to approximately 70% at 20 μM and 50% at 50 μM, indicating a concentration-dependent cytotoxic effect. Additionally, RNA-seq data analysis revealed upregulated PTPN11 expression in hepatocellular carcinoma tissues compared to normal liver. These quantitative findings strengthen the experimental evidence for Liensinine's inhibitory potential. Together, these findings suggest that Liensinine may serve as a natural SHP2 allosteric inhibitor with anticancer potential. Allosteric regulation ; Phosphatase ; Protein tyrosine phosphatase ; Virtual screening ; Alkaloid ; Enzyme ; Docking (animal) Chromatography in Natural Products ; Biological Activity of Diterpenoids and Biflavonoids ; Phytochemistry and Biological Activities
Authors: Ting Fu | Shuai Liu | Baoxin Niu | Wanfu Shen | Chunguang Hu | Ruixuan Peng | Kai Liu | Chengbao Jiang | Shengxue Yang
Source: Nano Research (article, 2025-01-03)
Field: Materials Science — Materials Chemistry

Low-power consumption anisotropic CMOS inverters based on n-ReS <sub>2</sub> and p-WSe <sub>2</sub> The surge in data volume and algorithmic complexity necessitates the development of highly integrated, low-power, and high-performance electronic components. Conventional complementary metal-oxide-semiconductor (CMOS) inverters, which rely solely on isotropic two-dimensional materials, encounter limitations due to their single voltage output, thereby impeding the miniaturization of integrated circuits. In this study, we introduce anisotropic CMOS inverters based on n-ReS<sub>2</sub> and p-WSe<sub>2</sub>, which demonstrate distinct voltage transfer characteristics across various crystalline orientations. These inverters exhibit the lowest voltage gain along the <em>a</em>-axis of ReS<sub>2</sub> flakes, whereas they possess the highest voltage gain and the lowest static power consumption along the <em>b</em>-axis. By optimizing the gate dielectric on substrates, the inverter achieves an enhanced voltage gain of 30.8 and an ultra-low power consumption of 5.4 pW along the b-axis direction, when fabricated on a 35 nm Al<sub>2</sub>O<sub>3</sub> substrate deposited via atomic layer deposition (ALD) method. Additionally, it captures a clear dynamic switching behavior at a supply voltage of 3 V under a 20 Hz square wave input signal. This study proposes a potential approach to circuit miniaturization by leveraging anisotropic two-dimensional materials for the integration of diverse voltage transfer characteristics within a single logic device, thereby achieving a combination of low power consumption and high-density integration. CMOS ; Power consumption ; Materials science ; Anisotropy ; Optoelectronics ; Power (physics) ; Nanotechnology ; Consumption (sociology) ; Electronic engineering ; Physics ; Engineering ; Optics ; Thermodynamics 2D Materials and Applications ; Chalcogenide Semiconductor Thin Films ; MXene and MAX Phase Materials
Authors: Kunanon Sakkampang | Piyorus Tasenhog | Nirut Onsalung | Narong Huchaiyaphum
Source: Civil Engineering Journal (article, 2025-02-01)
Field: Materials Science — Polymers and Plastics

A Study of Biomass Concrete Reinforced with Fiber Composites to Enhance Impact Load Capacity This research investigates the energy absorption from impact forces of steel reinforced concrete using fly ash obtained from agricultural processes, reinforced with glass fiber-reinforced polymer (GFRP) bars, compared to steel reinforcement. The reinforcement pattern involves incorporating GFRP bars into a square grid pattern of 4, 9, and 12 openings within bio-steel concrete with dimensions (W × L × H) of 40 × 40 × 10 cm. The testing is conducted using a Drop Test impact testing machine with a 30 kg hammer head at a velocity of 7 m/s, employing two different hammer head configurations: flat and 45-degree angled, to study energy absorption (Ea), specific energy absorption (Es), and the pattern of deformation resulting from impacts. The study finds that CBRHA-10-fiber A concrete exhibits higher energy absorption and specific energy absorption compared to steel-reinforced (CBRHA-10-steel A) concrete in the same configuration by 18.82% and 26.83%, respectively, in the flat-headed hammer impact configuration. Similarly, in the 45-degree angled hammer head configuration, CBRHA-10-fiber A concrete demonstrates superior energy absorption and specific energy absorption compared to steel reinforcement in the same configuration by 6.10% and 14.92%, respectively. In conclusion, bio-steel reinforced concrete with glass fiber-reinforced polymer (GRFP) reinforcement exhibits good load-bearing capacity and suitability as an alternative to steel reinforcement in future applications. Doi: 10.28991/CEJ-2025-011-02-020 Full Text: PDF Composite material ; Biomass (ecology) ; Materials science ; Fiber ; Agronomy Natural Fiber Reinforced Composites ; Soil Mechanics and Vehicle Dynamics ; Agricultural Engineering and Mechanization
Authors: Zhan Heng Ng | Tee Connie | Kan Yeep Choo | Michael Kah Ong Goh | Nur Aisyah Aziz | Hong Yeap Ngo
Source: Journal of Informatics and Web Engineering (article, 2025-02-14)
Field: Materials Science — Polymers and Plastics

Fiber Break Prevention Using Machine Learning Approaches Modern fiber-optic communication systems are built around optical fiber, which allows data to be sent by emitting infrared light pulses. It is widely used by telecommunications firms and is essential to the smooth transmission of information in internet communication as well as the transmission of telephone signals. Nonetheless, optical fibers intrinsic fragility raises a problem, especially in areas where building projects are taking place. Especially nowadays construction-related impact and crushing pressures can cause physical damage that jeopardizes the fiber optic's integrity. Therefore, this research emphasizes the necessity of taking preventative and mitigating actions to reduce the possibilities of fiber optic breakages in response to these difficulties by using machine learning approaches. The data collected by an optical fiber sensor and a distributed acoustic sensing interrogator unit (DAS). Five tools are used to simulate fiber break threats on the road surface and the fiber optic signal is denoised by using the bandpass Butterworth filter. The filtered data is then transformed into spectrogram representation and trained by using the machine learning approaches. The results of the experiments in the research achieves the accuracy 99.78% which is a high accuracy which can be potentially applied in classifying the signals of the tools and preventing the breakage of the fiber optic cables. Fiber ; Computer science ; Materials science ; Composite material Textile materials and evaluations ; Industrial Vision Systems and Defect Detection