jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Gabriel T Vercelli | Xingcheng Zhou | Stefany Moreno-Gámez | Rashi R. Jeeda | Rachel Gregor | Jonasz Słomka | Akorfa Dagadu | Ariel Furst | Otto X. Cordero
Source: Molecular Systems Biology (article, 2026-03-16)
Field: Chemistry — Organic Chemistry

A universal surface functionalization technique to chemically enhance live microbial cells Microbial surface functionalization is a powerful strategy for endowing microbes with novel, non-genetic functions. However, existing methods are often species-specific, limited in scope, and compromise cell viability. Here, we present a universal and modular platform for high-density, reproducible surface functionalization across diverse microbial species-including Gram-positive, Gram-negative, aerobic, and anaerobic bacteria-using multiple molecular classes such as fluorophores, enzymes, and nucleic acids. Our method preserves cell viability and achieves 50× higher functionalization efficiency than previous methods with a standardized protocol applicable to any azide-containing molecule. Applications of the method show reproducible and tunable phenotypic outcomes at the single-cell level: fluorophore labeling yielded adjustable fluorescence, β-lactamase conferred scalable antibiotic resistance, and DNA coatings modulated adhesion and aggregation. This platform provides quantitative, non-genetic control over microbial phenotypes and complements genetic engineering approaches. It enables new possibilities for microbial design in biotechnology, medicine, and environmental applications where genetic modification is impractical or undesirable. Surface modification ; Synthetic biology ; Modular design ; DNA ; Nucleic acid ; Phenotype Click Chemistry and Applications ; Cancer Research and Treatments ; Biochemical and Structural Characterization
Authors: Diallo Abdoul Kadri | Ndioukane Rémi | Diallo Alpha Issa | Sakho Adama Moussa | Toupouvogui Jean Ouere | Tall Abdoulaye | Bahmariama Bahmariama | Kobor Diouma
Source: International Journal of Advanced Research (article, 2026-02-28)
Field: Materials Science — Materials Chemistry

ELECTRICAL AND MORPHOLOGICAL CHARACTERIZATION OF SOLUTION-PROCESSED GEL PZN-PT PEROVSKITE NANOPARTICLES THIN FILM FOR TRANSISTOR DIELECTRICS Thin film filed effect transistors (TFTs) have drawn significant attention from scientific community as one of the most attractive for low-cost, large area and flexible electronics.In this context, the development of high-k dielectric materials with additional functional properties is essential for low voltage operating and enhancement of device performance. This work presents the investigation of solution processed Pb(Zn1/3Nb2/3)O3 4.5PbTiO3(PZN PT) perovskite nanoparticles thin films, with and without Mn doping, as gate dielectrics for thin film transistor applications. PZN-PT with 1% Mn doped and undoped PZN-PT films were deposited by spin coating on nanostructured doped n-type silicon substrate to form metal-oxide-semiconductor (MOS) structures with top silver contact. SEM analysis shows Mn doping PZN-PT presents a denser and more homogeneous microstructure compared to undoped. Electrical measurements confirmed transistor operating mode and highlighted the effect of ferroelectric polarization on device behavior. Spin coating ; Dielectric ; Thin film ; Transistor ; Perovskite (structure) ; Doping ; Substrate (aquarium) ; Silicon Ferroelectric and Piezoelectric Materials ; Chemical and Physical Properties of Materials ; Dielectric materials and actuators
Authors: B. Pranesh | M. Kanagaraj | R. Vaira Vignesh | P. Sathiya
Source: Koroze a ochrana materialu (article, 2026-02-01)
Field: Materials Science — Biomaterials

Numerical investigation of corrosion phenomena in heat-treated Mg-1Er-1Yb alloy via multiphysics modeling Abstract This study examines the galvanic corrosion behaviour of a Mg-1Er-1Yb alloy when coupled with 316L stainless steel, with particular emphasis on the influence of artificial aging. The alloy was solution-treated and aged at 200 °C for 5 h (HT5), 10 h (HT10) and 15 h (HT15), and compared with the base metal. Electrochemical characterization was performed in 3.5 wt.% NaCl using a three-electrode cell, with potentiodynamic scans conducted from −250 mV to +250 mV versus OCP at a scan rate of 1 mV s⁻¹. The Tafel parameters (βₐ, β c ), corrosion potential (E corr ), and corrosion current density (i corr ) were extracted for both the Mg alloy (anode) and 316L stainless steel (cathode), enabling a quantitative assessment of galvanic interaction. Among all conditions, HT10 exhibited the most favourable performance, with a significantly reduced i corr (1.22 × 10⁻⁵ A cm⁻²) and the lowest corrosion rate (12.9 mm year⁻¹), compared to 22.8 mm year⁻¹ for the base metal. These experimental findings were incorporated into a COMSOL Multi- physics model using the Arbitrary Lagrangian Eulerian (ALE) method to dynamically track corrosion front movement under galvanic coupling. The simulated electrolyte potential fields, local current density profiles, pit morphology and thickness loss showed strong agreement with immersion and polarization results, with deviations below 7%. The study demonstrates that controlled aging at 200 °C for 10 h leads to a refined microstructure and improved galvanic compatibility with 316L stainless steel. The integrated experimental numerical approach provides a reliable framework for designing rare-earth--based Mg alloys for sacrificial anode applications. Corrosion ; Alloy ; Tafel equation ; Multiphysics ; Galvanic cell ; Galvanic corrosion ; Microstructure ; Polarization (electrochemistry) Magnesium Alloys: Properties and Applications ; Hydrogen embrittlement and corrosion behaviors in metals ; Corrosion Behavior and Inhibition
Authors: S. K. Chakarvarti | A.S. Manocha
Source: Research Square (preprint, 2026-02-10)
Field: Materials Science — Materials Chemistry

Emerging Paradigms in Thermal Interface Materials:Metallic Nanostructure–Based Hybrid Architecture Chakravardhan 22(CV22)** Interface (matter) ; Thermal grease ; Thermal ; Electronics ; Fabrication ; Key (lock) ; Enabling ; Thermal management of electronic devices and systems Thermal properties of materials ; Phase Change Materials Research ; Advanced Thermoelectric Materials and Devices
Authors: Naiara Milagres Augusto da Silva | Juliana Junqueira Pinelli | Cínthia Caetano Bonatto | Luciano Paulino Silva
Source: Surfaces (article, 2026-02-24)
Field: Materials Science — Biomaterials

A Comparative Study of Dip-Coating and Drop-Casting for Surface Modification of Kraft Paper with Silver Nanoparticles Toward Active Packaging Kraft paper, commonly known as brown paper, has been widely used in the preservation of various food products and is increasingly explored in the development of active packaging materials with antimicrobial functionality by incorporating metal nanoparticles. This study aimed to comparatively investigate the surface modification of Kraft paper with silver nanoparticles (AgNPs) using dip-coating and drop-casting techniques. AgNPs were produced via green synthesis and incorporated onto the surface of Kraft paper samples. The modified samples were characterized using physicochemical techniques, including atomic force microscopy (AFM), Raman spectroscopy and light microscopy, as well as nanomechanical characterization via force spectroscopy. The antimicrobial activity of the modified papers was assessed using the disk diffusion method. The results demonstrated that the modification techniques resulted in distinct surface characteristics. Samples treated with the drop-casting method exhibited the highest AgNP surface loading; however, this was accompanied by pronounced surface heterogeneity and a tendency toward reduced load-bearing capacity. Overall, the findings indicate that the choice of deposition technique plays a key role in controlling nanoparticle distribution and surface properties. Within the limitations of the techniques evaluated, the incorporation of nanomaterials with potential antimicrobial activity into Kraft paper may offer opportunities for the development of active food packaging, although further optimization is required. Surface modification ; Silver nanoparticle ; Nanomaterials ; Kraft paper ; Antimicrobial ; Nanoparticle ; Food packaging ; Active packaging Nanocomposite Films for Food Packaging ; Material Properties and Processing ; Advanced Cellulose Research Studies
Authors: Devin Seay | I. P. Fernando | Dustin Keller
Source: arXiv (Cornell University) (article, 2026-03-10)
Field: Chemistry — Spectroscopy

Polarized Target Nuclear Magnetic Resonance Measurements with Deep Neural Networks Continuous-wave Nuclear Magnetic Resonance (CW-NMR) operated in constant-current mode has served as a foundational technique for polarization measurement in solid-state dynamically polarized targets within nuclear and high-energy physics experiments for several decades, and it remains an essential tool. Conventional Q-meter-based phase-sensitive detection is critical for precise real-time determination of target polarization during scattering runs. However, the accuracy and reliability of these measurements are frequently compromised by elevated noise levels, baseline drift, and systematic uncertainties arising from signal isolation and fitting, ultimately degrading the overall experimental figure of merit. In this work, we report the first successful application of neural network architectures to continuous-wave NMR polarization metrology. By leveraging advanced machine learning techniques for signal extraction and denoising, we achieve a substantial reduction of fitting uncertainties under a variety of realistic simulated and experimental conditions. These improvements translate directly into more robust real-time (online) polarization monitoring and significantly higher precision in subsequent offline analysis. The resulting methodology offers an improved figure of merit for scattering experiments employing dynamically polarized targets and establishes a new tools for NMR-based polarimetry in high-energy and nuclear physics. Polarization (electrochemistry) ; Polarimetry ; Artificial neural network ; Scattering ; Figure of merit ; Signal processing ; Induced polarization Advanced NMR Techniques and Applications ; Atomic and Subatomic Physics Research ; NMR spectroscopy and applications
Authors: Vladimir Maksimović | Branimir Jakšić
Source: ICCK Transactions on Electric Power Networks and Systems (article, 2026-02-27)
Field: Materials Science — Materials Chemistry

Automatic Pollution Detection on High-Voltage Isolators Using a Two-Phase Approach The accumulation of atmospheric and industrial pollution of high-voltage insulators is one of the frequent problems and a pattern of failures in transmission systems. In this paper, a two-phase approach based on deep learning is proposed for the detection of pollution of high-voltage insulators. The proposed approach automatically detects three types of pollution (salt, soot and excrement) based on UAV images. In addition to detection, the classification of pollution is automatically done into three levels (low, medium and high pollution). In the first phase, the You Only Look Once (YOLO) detector is used for precise detection and isolation of insulators, where an average accuracy of mAP@0.5 of 93.75% is achieved. The model was trained on a Merged Public Insulator Dataset (MPID) database containing over 5000 insulators. The second phase utilizes the Zenodo dataset, which contains over 14,000 synthetic insulator images. In the second phase, the model was trained using the EfficientNet-B0 convolutional network to classify the type and level of pollution. In order to solve the problem of real data, fine-tuning was done for all 10 classes. The results show that the accuracy is 88% on a partial classification of 10 levels of pollution. When using 4 levels of pollution, the model achieves an accuracy of 91%. Additional automation was achieved with priority metrics, which by analyzing 100 images showed 24% of critical cases. The system determines the cleaning priority based on the pollution intensity, ensuring that critical cases are addressed first. A comparative analysis was performed when the model was trained with MobileNetV2, ResNet16 and VGG. The results show that the proposed model with the highest recall minimizes the risk of missed critical insulators. For example, in real-world applications, MobileNetV2 has a larger difference, which means too many false positives, while ResNet18 has a smaller difference, which means more false negatives which is a security risk. Automation ; Pollution ; Insulator (electricity) ; Detector ; Transmission (telecommunications) ; Identification (biology) ; Deep learning ; Automated method High voltage insulation and dielectric phenomena ; Power Line Inspection Robots ; Advanced Neural Network Applications
Authors: Hanaa S. Omar | A. Mohamed | Nehad A El-Gammal | Warda Hussain | Saleh S. Alhewairini | Mahmoud Abdelfatah | Abdelhamed Elshaer | Reda Omara | Ayman F. Omar | Aly Derbalah
Source: Journal of Fungi (article, 2026-04-01)
Field: Materials Science — Materials Chemistry

MgO and Zn/MgO Nanoparticles as Direct Antifungal Compounds and Elicitors Against Sclerotinia sclerotiorum in Pea The purpose of this study was to assess the effectiveness of two nanostructures (MgO and Zn/MgO) against Sclerotinia sclerotiorum, which causes white mold disease in peas, as direct antifungal agents or resistance inducers in pea plants. The direct antifungal activity of these nanostructures was evaluated by assessing their ability to inhibit S. sclerotiorum growth in vitro and reduce white mold severity in the greenhouse. The induction of resistance in pea plants was examined by assessing the expression of three defense-related genes using quantitative real-time PCR and measuring the phenolic compounds content in treated pea plants relative to untreated controls. The effect of the tested control agents on the growth and yield of pea plants was investigated. In comparison to the untreated control, S. sclerotiorum growth was markedly suppressed following treatment with the investigated compounds. The complete suppression (100%) of S. sclerotiorum growth was achieved with concentration levels of 100 mg/L for both MgO and Zn/MgO nanostructures. In greenhouse conditions, pea plants treated with the investigated chemicals showed a considerable reduction in the severity of white mold disease when compared to the untreated control plants. The transcript levels of 12-oxophytodienoate reductase 11 (OPR1), antioxidant peroxide (PsOXII), and chlorophyll a-b binding protein genes increased significantly in treated plants with MgO (3.1, 2.7, and 3.5-fold), fungicide (3.2, 2.8, and 2.8-fold), and Zn/MgO (3.5, 3, and 5-fold) compared to control, respectively. Pea plants treated with the tested nanoparticles generated more phenolic content than untreated controls. The application of fungicide and tested nanoparticles to peas greatly enhanced their growth properties. In light of our results, the application of these nanoparticles may represent a novel approach for controlling this pathogen. Sclerotinia sclerotiorum ; Fungicide ; Plant disease resistance ; Antioxidant ; Growth inhibition ; Antifungal ; Mold ; Nitrate reductase Nanoparticles: synthesis and applications ; Plant Growth Enhancement Techniques ; Plant-Microbe Interactions and Immunity
Authors: Sho Nakayama | Eric J. Shepard | Abhinandan Banerjee | Xiaoda Yang | Debbie C. Crans
Source: Molecules (article, 2026-03-11)
Field: Materials Science — Materials Chemistry

Applications of Carbon Dots and Graphene Quantum Dots in Treatment of Diabetes Carbon nanoparticles (CNPs) are increasingly being considered for medical applications. The objective of this article is to determine which anti-diabetic drugs and compounds have been enhanced by CNPs, and which CNP scaffolds were found to be successful. The anti-diabetic drugs administered loaded on CNPs include insulin, metformin, glimepiride and vanadium compounds. Carbon quantum dots (CQDs), graphene quantum dots (GQDs), graphene oxide quantum dots (GOQDs), hybrid systems and fullerenes are all carriers able to alleviate symptoms of diabetes. Successful CNPs are 10 nm or less and can have a flat pancake structure, as well as the spherical CQDs and the spherical-but-hollow gadofullerene (Gd-C82). The use of the carbon nanoparticle scaffold includes oral, intravenous administration and placement as an implant in a diabetic animal model system. In vitro studies in an insulin-resistant model demonstrate a 500-1000-fold enhancement of metformin when placed on the pegylated GOQD. Although some CNPs have low toxicity, more information is needed for understanding the metabolism associated with uptake and processing. In summary, CNPs represent a novel class of nanoparticles that has promising potential. They enhance the efficacy of anti-diabetic drugs, have low toxicity, and keep the loaded drug protected until reaching their targets. Graphene ; Quantum dot ; Carbon quantum dots ; Nanoparticle ; Nanomedicine ; Fullerene ; Carbon fibers ; Glimepiride Carbon and Quantum Dots Applications ; Graphene and Nanomaterials Applications ; Electrochemical sensors and biosensors
Authors: Jie Zhang | Shuaiqian Li | Zhaowen Du | Bin Wang | Zhuoyue Wen
Source: Scientific Reports (article, 2026-03-24)
Field: Materials Science — Materials Chemistry

Study on corrosion behavior and mechanical performance degradation prediction of bolts in high mineralized corrosion environment Deep coal mining faces high mineralized corrosion environment. To explore the corrosion characteristics of bolts in deep coal mining, the corrosion behavior and performance degradation law of ordinary bolts and new zinc-infiltrated bolts in high mineralized corrosion environment were studied by corrosion experimental, XRD and tensile test, the time-varying model for bolts performance degradation was established. The results show that: The corrosion morphologys of bolts in high mineralized corrosion environment are pitting corrosion. The ion erosion resistance of zinc-infiltrated bolts is significantly stronger than that of ordinary bolts. The increase of ion concentration will aggravate the performance degradation of bolts. The corrosion depth of bolts is linearly positively correlated with their strength loss. Based on the idea of number prediction and size homogenization of corrosion pits, the time-varying model for bolts performance degradation was derived by combining experimental data, pitting corrosion model and Weibull distribution. Based on the model analysis, it was found that the Cl− concentration and temperature are exponentially negatively correlated with the service life of bolts. Finally, the anti-corrosion support recommendations centered on the zinc-infiltration process were proposed. Corrosion ; Degradation (telecommunications) ; Ultimate tensile strength ; Weibull distribution ; Pitting corrosion ; Service life Corrosion Behavior and Inhibition ; Hydrogen embrittlement and corrosion behaviors in metals ; Erosion and Abrasive Machining
Authors: Md. Ahasan Kabir | I. Lee | Sang-Heon Lee
Source: Toxins (article, 2026-02-02)
Field: Chemistry — Analytical Chemistry

Squeeze-Excitation Attention-Guided 3D Inception ResNet for Aflatoxin B1 Classification in Almonds Using Hyperspectral Imaging Almonds are a highly valued nut due to their rich protein and nutritional content. However, they are vulnerable to aflatoxin B1 (AFB1) contamination in warm and humid environments. Consumption of AFB1-contaminated almonds can pose serious health risks, including kidney damage, and may lead to significant economic losses. Consequently, a rapid and non-destructive detection method is essential to ensure food safety by identifying and removing contaminated almonds from the supply chain. Hyperspectral imaging (HSI) and 3D deep learning provide a non-destructive, efficient alternative to current AFB1 detection methods. This study presents an attention-guided Inception ResNet 3D Network (AGIR-3DNet) for fast and precise detection of AFB1 contamination in almonds utilizing HSI. The proposed model integrates multi-scale feature extraction, residual learning, and attention mechanisms to enhance spatial-spectral feature representation, enabling more precise classification. The proposed 3D model was rigorously tested, and its performance was compared against 3D Inception and various conventional machine learning models. Compared to conventional machine learning models and deep learning architectures, AGIR-3DNet outperformed and achieved superior validation accuracy of 93.30%, an F1-score (harmonic mean of precision and recall) of 0.94, and an area under the receiver operating characteristic curve (AUC) value of 0.98. Furthermore, the model enhances processing efficiency, making it faster and more suitable for real-time industrial applications. Hyperspectral imaging ; Deep learning ; Aflatoxin ; Residual neural network ; Feature (linguistics) ; Residual Spectroscopy and Chemometric Analyses ; Mycotoxins in Agriculture and Food ; Remote-Sensing Image Classification
Authors: Changyu Yao | Haochen Shen | Zhongyuan Liu | Ruotian Gong | Md Shakil Bin Kashem | Stella Varnum | Liangyu Li | Hangyue Li | Yue Yu | Yizhou Wang | Xiaoshui Lin | Jonathan R. Brestoff | Chenyang Lu | Shankar Mukherji | Chuanwei Zhang | Chong Zu
Source: ArXiv.org (article, 2026-03-16)
Field: Materials Science — Materials Chemistry

A Deep-Learning-Boosted Framework for Quantum Sensing with Nitrogen-Vacancy Centers in Diamond Nitrogen-vacancy (NV) centers in diamond are a versatile quantum sensing platform for high sensitivity measurements of magnetic fields, temperature and strain with nanoscale spatial resolution. A common bottleneck is the analysis of optically detected magnetic resonance (ODMR) spectra, where target quantities are encoded in resonance features. Conventional nonlinear fitting is often computationally expensive, sensitive to initialization, and prone to failure at low signal-to-noise ratio (SNR). Here we introduce a robust, efficient machine learning (ML) framework for real-time ODMR analysis based on a one-dimensional convolutional neural network (1D-CNN). The model performs direct parameter inference without initial guesses or iterative optimization, and is naturally parallelizable on graphics processing units (GPU) for high-throughput processing. We validate the approach on both synthetic and experimental datasets, showing improved throughput, accuracy and robustness than standard nonlinear fitting, with the largest gains in the low-SNR regime. We further validate our methods in two representative sensing applications: diagnosing intracellular temperature changes using nanodiamond probes and widefield magnetic imaging of superconducting vortices in a high-temperature superconductor. This deep-learning inference framework enables fast and reliable extraction of physical parameters from complex ODMR data and provides a scalable route to real-time quantum sensing and imaging. Robustness (evolution) ; Sensitivity (control systems) ; Convolutional neural network ; Nonlinear system ; Bottleneck ; Scalability ; Quantum Diamond and Carbon-based Materials Research ; High-pressure geophysics and materials ; Atomic and Subatomic Physics Research