jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Daqian Wan | Haiqing Tian | Lina Guo | Kai Zhao | Yang Yu | Xuebo Zheng | Haijun Li | Jianying Sun
Source: Agriculture (article, 2025-07-13)
Field: Chemistry — Analytical Chemistry

Color-Sensitive Sensor Array Combined with Machine Learning for Non-Destructive Detection of AFB1 in Corn Silage Aflatoxin B1 (AFB1) contamination in corn silage poses significant risks to livestock and human health. This study developed a non-destructive detection method for AFB1 using color-sensitive arrays (CSAs). Twenty self-developed CSAs were employed to react with samples, with reflectance spectra collected using a portable spectrometer. Spectral data were optimized through seven preprocessing methods, including Standard Normal Variate (SNV), Multiplicative Scatter Correction (MSC), first-order derivative (1st D), second-order derivative (2nd D), wavelet denoising, and their combinations. Key variables were selected using five feature selection algorithms: Competitive Adaptive Reweighted Sampling (CARS), Principal Component Analysis (PCA), Random Forest (RF), Uninformative Variable Elimination (UVE), and eXtreme Gradient Boosting (XGBoost). Five machine learning models were constructed: Light Gradient Boosting Machine (LightGBM), XGBoost, Support Vector Regression (SVR), RF, and K-Nearest Neighbor (KNN). The results demonstrated significant AFB1-responsive characteristics in three dyes: (2,3,7,8,12,13,17,18-octaethylporphynato)chloromanganese(III) (Mn(OEP)Cl), Bromocresol Green, and Cresol Red. The combined 1st D-PCA-KNN model showed optimal prediction performance, with determination coefficient (Rp2 = 0.87), root mean square error (RMSEP = 0.057), and relative prediction deviation (RPD = 2.773). This method provides an efficient solution for silage AFB1 monitoring. Silage ; Agronomy ; Environmental science ; Food science ; Biology Spectroscopy and Chemometric Analyses ; Identification and Quantification in Food ; Smart Agriculture and AI
Authors: Vangala Lavanya | G S Annammadevi
Source: International Journal of Drug Delivery Technology (article, 2025-09-25)
Field: Materials Science — Materials Chemistry

Enhancement of Solubility and Dissolution of Azilsartan using Liquisolid Technology: Formulation, Optimization and Evaluation The Liquisolid technique was employed to enhance the solubility and dissolution of Azilsartan, an angiotensin II receptorblocker with poor aqueous solubility. A 2² full factorial design was utilized to optimize the formulation parameters,particularly focusing on FUJ and Croscarmellose Sodium concentrations. Solubility studies conducted in non-volatilesolvents (Labrasol, Captisol, Transcutol HP, and Capryol) identified Labrasolas the most effective solvent (96.37±0.82mg/mL), while Fujicalin exhibited the highest solubility enhancement (0.42 ± 0.02 mg/mL) among different carriers.The optimized formulation, containing 50mg FUJ and 5% Croscarmellose Sodium, demonstrated significantly improveddrug release compared to the pure drug. FTIR analysis confirmed no significant drug-excipient interactions, ensuring thestability of the formulation. Pre-compression and post-compression parameters, including Carr’s Index (13.85 ± 0.30%),Hausner’s Ratio (1.16 ± 0.01), hardness (5.6 ± 0.2 kg/cm²), friability (0.78 ± 0.03%), and disintegration time (65.2 ± 2.1sec, n=3), confirmed the formulation’s suitability for tablet manufacturing. Stability studies conducted per ICH guidelinesdemonstrated that the optimized formulation remained stable for three months.The study confirms that the Liquisolidtechnique significantly improves the solubility and dissolution of Azilsartan. Solubility ; Dissolution ; Croscarmellose sodium ; Factorial experiment ; Friability ; Solvent Crystallization and Solubility Studies
Authors: Miftahul Khairati | Apsari Puspita Aini | Enny Nurmalasari | Agung Kurnia Yahya
Source: Eksergi (article, 2025-04-15)
Field: Chemistry — Organic Chemistry

Comparison of Different Types of Bleaching Earth on the Quality of Bleaching Palm Oil (BPO) Bleaching Earth (BE), primarily derived from Bentonite, is widely used in the bleaching process of Crude Palm Oil (CPO). Its efficiency depends on the proportions of SiO₂, and Al₂O₃, which enable effective pigment absorption. This study evaluates the performance of unmodified Bentonite, nano SiO₂-modified Bentonite, and commercial BE in producing Bleached Palm Oil (BPO). The novelty lies in the nano SiO₂ modification, designed to enhance the structural properties and absorption capabilities of Bentonite. Characterization of BE was conducted using Fourier Transform Infrared Spectroscopy (FTIR) and Scanning Electron Microscopy (SEM), while BPO quality was assessed through parameters like moisture content, acid value, Free Fatty Acid (FFA) levels, (Deterioration of Bleachability Index (DOBI), and β carotene concentration. The results showed that BPO processed with nano SiO₂-modified Bentonite had the lowest β carotene concentration (443%) and a DOBI value of 1.453, demonstrating superior bleaching performance compared to unmodified and commercial BE. Other parameters, including FFA levels and moisture content, exhibited minimal variation. Color analysis revealed that BPO processed with nano SiO₂-modified Bentonite achieved a comparable color value (1.5/15) to commercial BE. These findings highlight the potential of nano SiO₂-modified bentonite for improving oil quality by reducing FFA and acid value while maintaining optimal moisture content, offering a novel and effective alternative to unmodified bentonite in oil refining. Palm oil ; Quality (philosophy) ; Pulp and paper industry ; Palm ; Chemistry ; Environmental science ; Food science ; Engineering Edible Oils Quality and Analysis ; Adsorption and biosorption for pollutant removal ; Biodiesel Production and Applications
Source: International Journal of Corrosion and Scale Inhibition (article, 2025-12-27)
Field: Materials Science — Materials Chemistry

Microbiologically assisted stress corrosion cracking of pipeline steel. A review The article analyzes microbiologically assisted stress corrosion cracking (SCC) of pipeline steel, with a focus on sulfate-reducing bacteria (SRB).SCC is a particularly dangerous type of corrosion damage to steel.The SCC of metals occurs under the simultaneous action of static tensile stress (external or internal) and a corrosive environment.This article provides a review of available information on SRB-assisted cracking, including real-life cases, laboratory investigations, crack mechanisms, and influencing factors.Much attention is given to the pipeline steels used in marine media.Summing up the results, it can be concluded that the rates of crack growth due to corrosion fatigue in saltwater environments can be significantly enhanced by the presence of biologically produced H2S from SRB.The potential role of FeS-containing biofilms formed on the surface of pipeline steels in aqueous media in the promotion of SRBinduces corrosion is described.It has been found that SRB affects the entire corrosion process such as biofilm formation, pitting, hydrogen embrittlement (HE), and SRB-assisted cracking.Methods of protection and inhibitors preventing SRB-assisted cracking are reviewed. Stress corrosion cracking ; Corrosion ; Pipeline (software) ; Stress (linguistics) ; Cracking Corrosion Behavior and Inhibition ; Structural Integrity and Reliability Analysis ; Hydrogen embrittlement and corrosion behaviors in metals
Authors: Yunhui Xu | Min Deng | Hui Tu | Zaheer ul Haq | Haifeng Xu | Xin Zhang
Source: Materials Research (article, 2025-01-01)
Field: Materials Science — Surfaces, Coatings and Films

Research on the Automatic Recognition Method of Micro-nano Regeneration Rubber Filler Dispersion in Scanning Electron Microscope Images Micro-nano regenerative rubber represents a significant advancement over traditional regenerative rubber by eliminating the need for polluting chemical agents such as coal tar, asphalt, and regenerators. The performance of composite materials filled with micro-nano regenerative rubber depends critically on the degree of filler dispersion. However, current methods for assessing dispersion rely on subjective visual estimation of electron microscope images, which lack quantitative precision and are prone to human error, especially given the complex distribution of micro-nano particles. This study introduces a novel, automated method for grading the dispersion of micro-nano regenerative rubber fillers in scanning electron microscope (SEM) images, leveraging advanced image processing techniques. The proposed method automatically identifies and quantifies the dispersion of micro-nano regenerative rubber clusters through particle size and distribution analysis. By establishing objective grading rules for dispersion levels, it provides a reliable and efficient alternative to traditional subjective assessments. This innovative approach not only enhances the accuracy of dispersion evaluation but also facilitates the development of high-performance composite materials by enabling precise control over filler distribution. The method's effectiveness is validated through comprehensive experimental analysis, demonstrating its potential to significantly improve the quality and consistency of micro-nano regenerative rubber applications in various industries. Scanning electron microscope ; Natural rubber ; Dispersion (optics) ; Materials science ; Regeneration (biology) ; Filler (materials) ; Nano- ; Electron microscope ; Composite material ; Electron ; Optics ; Physics ; Biology Electron and X-Ray Spectroscopy Techniques ; Industrial Vision Systems and Defect Detection ; Surface Roughness and Optical Measurements
Authors: Zixi Liu
Source: Applied and Computational Engineering (article, 2025-07-11)
Field: Materials Science — Polymers and Plastics

Computational Design and Temporal Signal Analysis for Wearable Smart Textiles: AI-Driven Sensor Layout Optimization and Deep Learning-Based Anomaly Detection Smart wearable fabrics are breaking through the technical bottlenecks of traditional health monitoring. The key lies in solving the adaptation problem between sensor layout and dynamic perception. By targeting pain points such as signal distortion and high false detection rate existing in current schemes, this study builds a new framework driven by the dual engines of algorithm design + deep learning. Through a hybrid optimization model integrating genetic algorithm and reinforcement learning, the global search for optimal solutions for multi-objective parameters (constraint coverage, signal redundancy, and wearing comfort) was realized for the first time, and the best sensor array layout scheme was found. At the end of data processing, the bidirectional long short-term memory (Bi-LSTM) network based on the attention mechanism can accurately capture abnormal fluctuations in time series data, complete diagnostic decisions within 120 milliseconds, and the detection accuracy rate reaches 95.4%, showing stronger anti-environmental interference capability compared to traditional methods. These breakthroughs pave the way for the practical application of smart clothing, especially in scenarios with strict real-time requirements such as sports rehabilitation and elderly monitoring, where the application potential is obvious. Further research will focus on integrating model lightweighting and autonomous power supply to further improve the energy efficiency ratio and wearable adaptability of the system. Anomaly detection ; Wearable computer ; Computer science ; SIGNAL (programming language) ; Wearable technology ; Deep learning ; Anomaly (physics) ; Artificial intelligence ; Pattern detection ; Human–computer interaction ; Embedded system Textile materials and evaluations ; Color perception and design
Authors: Cong Zhang | Jiaju Wu | Huazheng Wu | Yufei Liu | Xu Yang | Na Liu | Chaoyang Wang | Peipei Chen | Chenggang Yan | Seng Yang | Xingguang Liu | Shaowei Jiang
Source: ArXiv.org (preprint, 2025-12-11)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Ultrahigh-Q chiral resonances empowered by multi-head attention deep learning High quality (Q) factor optical chiral resonators are indispensable for many chiral photonic devices. Designing ultrahigh Q-factors in chiral metasurfaces traditionally relies on extensive parameter scanning, which is time-consuming and inefficient. While deep learning now provides a rapid design alternative, conventional models still face challenges in accurately predicting ultrahigh Q-factor spectral characteristics. In this study, we introduce a multi-head attention network (MuHAN) to accelerate the design of ultrahigh Q-factor optical chiral resonators in bilayer metasurfaces. MuHAN achieves forward spectral predictions in approximately 10ms, thousands of times faster than finite-difference time-domain simulations, boasting 99.85% and 99.9% accuracy for forward and inverse predictions, respectively. By transferring the learned physical principles, we perform inverse design of nanoscale structures with ultrahigh Q-factors (up to 2.9910E5) based on chiral quasi-bound states in the continuum (quasi-BICs) at minimal computational cost. Our rapid design tool, based on MuHAN, enables high-performance encryption imaging, bridging deep learning with high-Q chiral metasurfaces for advanced sensing, laser, and detection applications. Deep learning ; Bridging (networking) ; Resonator ; Photonics ; Inverse ; Quality (philosophy) Metamaterials and Metasurfaces Applications ; Plasmonic and Surface Plasmon Research ; Neural Networks and Reservoir Computing
Authors: P.D. Willenshofer | Matheus A. Tunes | H.T. Vo | Lukas Stemper | Markus Alfreider | Oliver Renk | Graeme Greaves | Daniel Kiener | Peter J. Uggowitzer | Stefan Pogatscher
Source: Advanced Materials (article, 2025-12-15)
Field: Materials Science — Materials Chemistry

Radiation‐Resistant Aluminum Alloy for Space Missions in the Extreme Environment of the Solar System Future human exploration of the solar system demands advanced materials capable of withstanding extreme environments, particularly exposure to solar energetic particle radiation. Current material selection criteria for space applications prioritize a high strength-to-weight ratio, high corrosion resistance and manufacturability, favoring age-hardenable Al-based alloys. However, conventional precipitation-hardened Al alloys suffer from irradiation-assisted dissolution of strengthening phases at doses as low as 0.2 displacements-per-atom (dpa), undermining their performance. Furthermore, these alloys develop radiation-induced defects, such as dislocation loops and voids, even at low doses. This study presents a novel ultrafine-grained (UFG) Al-based alloy, designed using the crossover alloying concept and strengthened by T-phase precipitates, featuring a chemically-complex structure with 162 atoms in its unit cell composed of Mg<sub>32</sub>(Zn,Al)<sub>49</sub>. It is showed that T-phase precipitates have exceptional radiation tolerance up to 24 dpa. Owing to the nanoscale UFG structure, dislocation loops are suppressed, and voids are only observed beyond 75 dpa. Microtensile tests up to 20 dpa confirm the preservation of mechanical performance under irradiation. The results underline the potential of this alloy as a radiation-resistant, lightweight material for future space applications. Three key strategies enable this performance: (i) stabilization of a UFG microstructure, (ii) T-phase precipitation featuring a highly negative Gibbs free energy and chemically-complex giant unit cell, and (iii) precise process control to prevent grain growth during heat treatment and irradiation. Alloy ; Dislocation ; Aluminium ; Corrosion ; Precipitation ; Grain boundary ; Dissolution ; Space environment Fusion materials and technologies ; High-Velocity Impact and Material Behavior ; Magnesium Alloys: Properties and Applications
Authors: Kosaku Kato | Shiyu Feng | Zixi Zhao | Verdad C. Agulto | Ami Mizui | Takaaki Kasuga | Shun Ishioka | Masaya Nogi | Ichiro Ota | Motoharu Haga | Minoru Ueshima | Makoto Nakajima
Source: Advanced Materials Technologies (article, 2025-11-26)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Cellulose‐Nanofiber‐Based Layered Sub‐Terahertz Absorbing Sheets for B5G/6G Applications ABSTRACT For the sustainable development of beyond fifth‐generation (B5G) or sixth‐generation (6G) wireless communication technologies operating in the sub‐terahertz and terahertz range, it is highly desirable to utilize environmentally friendly materials in B5G/6G devices. Here, we report a sub‐terahertz absorbing sheet made of biodegradable cellulose‐nanofiber (CNF), which can be useful for achieving electromagnetic compatibility (EMC) in B5G/6G devices in a green manner. The absorber consists of one pure CNF layer sandwiched between two layers of carbon‐nanotube (CNT)‐dispersed CNF layers. The CNT concentration is engineered to be low (0.15 wt.%) in the layer on the wave‐incident side and high (6 wt.%) in the layer on the opposite side, emulating the resistive and reflective layers of a Salisbury screen absorber, respectively. The total thickness of our absorber is as thin as 120 µm. Terahertz time‐domain spectroscopy (THz‐TDS) reveals its good shielding effectiveness of 28.5 dB, low reflectance of 2.5%, and high absorptance of 97.4% at 300 GHz. Additionally, an absorptance higher than 80% is achieved over a broadband frequency range from 250 to 480 GHz. Good agreement between the measured results and calculated results based on the ABCD matrix method confirms that the cancellation of reflected waves is realized as designed. Absorptance ; Resistive touchscreen ; Terahertz radiation ; Broadband ; Electromagnetic shielding ; Reflectivity ; Overheating (electricity) Electromagnetic wave absorption materials ; Advanced Antenna and Metasurface Technologies ; Metamaterials and Metasurfaces Applications
Authors: Marcin Szyniszewski | Elaheh Mostaani | Angelika Knothe | V. V. Enaldiev | Andrea C. Ferrari | Vladimir I. Fal’ko | N. D. Drummond
Source: ArXiv.org (preprint, 2025-01-27)
Field: Materials Science — Materials Chemistry

Adhesion and Reconstruction of Graphene/Hexagonal Boron Nitride Heterostructures: A Quantum Monte Carlo Study We investigate interlayer adhesion and relaxation at interfaces between graphene and hexagonal boron nitride (hBN) monolayers in van der Waals heterostructures. The adhesion potential between graphene and hBN is calculated as a function of local lattice offset using diffusion quantum Monte Carlo methods, which provide an accurate treatment of van der Waals interactions. Combining the adhesion potential with elasticity theory, we determine the relaxed structures of graphene and hBN layers at interfaces, finding no metastable structures. The adhesion potential is well described by simple Lennard-Jones pair potentials that we parameterize using our quantum Monte Carlo data. Encapsulation of graphene between near-aligned crystals of hBN gives rise to a moiré pattern whose period is determined by the misalignment angle between the hBN crystals superimposed over the moiré superlattice previously studied in graphene on an hBN substrate. We model minibands in such supermoiré superlattices and find them to be sensitive to a 180$^\circ$ rotation of one of the encapsulating hBN crystals. We find that monolayer and bilayer graphene placed on a bulk hBN substrate and bulk hBN/graphene/bulk hBN systems do not relax to adopt a common lattice constant. The energetic balance is much closer for free-standing monolayer graphene/hBN bilayers and hBN/graphene/hBN trilayers. The layers in an alternating stack of graphene and hBN are predicted to strain to adopt a common lattice constant and hence we obtain a new, stable three-dimensional crystal with a unique electronic structure. Graphene ; Heterojunction ; Monte Carlo method ; Hexagonal boron nitride ; Materials science ; Adhesion ; Nanotechnology ; Hexagonal crystal system ; Quantum ; Boron nitride ; Condensed matter physics ; Optoelectronics ; Physics ; Composite material ; Chemistry ; Quantum mechanics ; Crystallography ; Mathematics Graphene research and applications ; Boron and Carbon Nanomaterials Research ; Diamond and Carbon-based Materials Research
Authors: Jan Sami
Source:  (article, 2025-09-03)
Field: Materials Science — Materials Chemistry

Advances and Gaps in the Application of Mixture Models Across Disciplines: A Comprehensive Review Mixture models have long served as a fundamental tool for analyzing heterogeneous data across a variety of disciplines. This review synthesizes recent methodological advances, highlights key applications, and identifies cross-domain gaps that present Data science ; Computer science ; Management science ; Engineering Crystallization and Solubility Studies
Authors: Melika Tajipour | Bojing Jiang | Leo Penny | Pallavi Dubey | Hayley Wabiszewski | Fuzhong Zhang | Marcus Foston | Mark Mba Wright
Source: Green Chemistry (article, 2025-12-16)
Field: Materials Science — Biomaterials

Lifecycle cost, environmental, and machine-learning value assessment for synthetic spider silk production from <i>E. coli</i> Synthetic spider silk biomaterials with exceptional strength and thermal resistance have attracted growing interest for various applications, including the textile and medical industries. Spider silk ; SILK ; Spider ; Production (economics) ; Textile ; Breaking strength Silk-based biomaterials and applications ; Silkworms and Sericulture Research ; Insect Utilization and Effects
Authors: Flora Tavernier | Elias Motelica-Heino | Miguel Thomas | Theresa Herbold | Mengyao Shi | Loïc Le Cunff | Charles Romieu | Vincent Segura
Source: Quantitative Plant Biology (article, 2025-01-01)
Field: Chemistry — Analytical Chemistry

<i>In situ</i> near-infrared non-destructive monitoring of sugar accumulation reveals that single berries ripening takes only 20 days Understanding how climate change impacts berry ripening physiology is essential for selecting genotypes that balance sugars and acids under warming conditions. In this context, we used a portable near-infrared spectrometer in the vineyard, to monitor sugar and acid evolution in individual berries from 10 grapevine varieties over two years. Spectra were periodically acquired on the same berries all along ripening, and a subset of these berries was also collected for sugars and organic acids quantification by HPLC, to train partial least square regression models. Prediction models for glucose, fructose, and malic acid concentrations were characterized by validation R<sup>2</sup> of 0.71, 0.64, and 0.55, respectively. We further used these models to follow sugar accumulation in individual berries and observed that single berries ripen two times faster than found in samples composed of multiple berries. Our results pave avenues toward precise quantitative approaches on sugar and acid fluxes in berry ripening studies. Ripening ; Berry ; Sugar ; Malic acid ; Organic acid ; Flavor Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research