jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Xiaoqiong Tan | Qiankun Ni | Ting Luo | Ki Taek Nam | Hong-Bo Cheng | Paul J. Dyson | Xing-Jie Liang | Juyoung Yoon
Source: Chemical Society Reviews (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

Photoswitchable imaging contrast agents as an emerging frontier in precision bioimaging Photoswitchable molecules exhibit reversible photoisomerization between distinct isomeric states under alternating light irradiation, possessing unique photophysical properties. Derived from these molecules, photoswitchable imaging contrast agents (PICAs) offer significant advantages over conventional agents: (1) background suppression through state-switching algorithms; (2) continuous imaging capability enabled by high fatigue resistance; (3) rapid signal modulation minimizing acquisition time. A comprehensive analysis of PICA advancements and persistent challenges across bioimaging applications is imperative. This review systematically evaluates several representative photoswitchable molecules as PICAs and proposes innovative molecular design and assembly strategies to address the limitations hindering the widespread implementation of PICAs in bioimaging, such as hydrophobicity, short-wavelength excitation, and suboptimal emission performance. Additionally, recent advances in PICAs for fluorescence, phosphorescence, photoacoustic, magnetic resonance and Raman imaging are summarized, highlighting their significant contributions to improving bioimaging quality and precision. Finally, an outlook is provided on the intersection of PICA design and property optimization for propelling both preclinical physiological research and clinical diagnostic and monitoring technologies. Optical imaging ; Photoisomerization ; Molecular imaging ; Aggregation-induced emission ; Intersection (aeronautics) ; SIGNAL (programming language) Photochromic and Fluorescence Chemistry ; Molecular Sensors and Ion Detection ; Click Chemistry and Applications
Authors: Hafiz Muhammad Rehan Tariq | Tea‐Sung Jun
Source: Han-guk hyeonmigyeong hakoeji/Applied microscopy (article, 2026-02-12)
Field: Materials Science — Biomaterials

Advances in EBSD-based approaches for quantifying slip activity in deformed Mg alloys Plastic deformation in Mg alloys requires a full understanding of slip activity and intergranular interactions, which determine mechanical behavior and strain localization. Electron backscatter diffraction (EBSD) has emerged as a versatile technique to map crystallographic orientations, slip systems, and lattice rotations, permitting the systematic analysis of deformation mechanisms across polycrystalline aggregates. The coupling of EBSD with metrics including Schmid factors, ingrain misorientation axes, and slip-transfer criteria permits a quantitative assessment of slip compatibility and the role of grain boundaries in strain accommodation. Limitations related to conventional 2D surface characterization create a growing need for novel three-dimensional techniques that can accurately represent grain boundary geometry as well as complex intergranular deformation pathways. A focused review of such methodologies will compile current knowledge on these methods and their capabilities and limitations, guiding future investigations toward a deeper understanding of microstructure-mechanics relationships in Mg alloys. Misorientation ; Electron backscatter diffraction ; Intergranular corrosion ; Slip (aerodynamics) ; Grain boundary ; Crystallite ; Plasticity Magnesium Alloys: Properties and Applications ; Microstructure and mechanical properties ; Metallurgical and Alloy Processes
Authors: Sherin F. Hammad | Doaa N. Kamel | Amira H. Kamal | Safa M. Megahed
Source: Luminescence (article, 2026-02-01)
Field: Materials Science — Materials Chemistry

Development of Sustainable Nitrogen‐Doped Carbon Quantum Dot Nanoprobe Using Red Onion for Ultrasensitive Estimation of Fluoride Ion in Human Saliva A quick, eco-friendly, and economical approach for the synthesis of self-nitrogen doped carbon quantum dots (N-CQDs) utilizing onion extract was created in the current study. This method used a microwave to heat an onion for 4 min, providing a sustainable source of carbon and nitrogen. The provided N-CQDs exhibited great stability and a fluorescence quantum yield of 12.1% at λex/λem of 340/430 nm, respectively. The produced N-CQDs served as a successful nanoprobe for fluoride measurement, within a concentration range of 20-300 ng/mL with a detection limit of 4.557 ng/mL. In the presence of several potentially conflicting medications, the N-CQDs showed remarkable fluoride selectivity. For the assessment of water's fluoride content, a mean percentage recovery of 100.955 ± 0.416 was obtained. The N-CQDs were employed to measure fluoride in human saliva within the 50-1000 ng/mL concentration range, with a limit of detection of 15.693 ng/mL. Making use of AGREE and complexGABI and complexMoGABI approach, the suggested probe's greenness was assessed. Its good greenness was ascribed to the use of a low-cost plant, low-energy consumption through microwave-assisted synthesis, and avoiding dangerous chemicals and organic solvents. The blueness of the developed approach was assessed using the Blue Applicability Grade Index (BAGI). Fluoride ; Nanoprobe ; Detection limit ; Quantum yield ; Quantum dot ; Fluorescence ; Carbon fibers ; Human health Carbon and Quantum Dots Applications ; Molecular Sensors and Ion Detection ; Fluoride Effects and Removal
Authors: Coral Ortiz | Nikita Dapurkar | Vicente Alegre | Francisco Rovira-Más
Source: Sensors (article, 2026-02-09)
Field: Chemistry — Analytical Chemistry

Image-Based Segregation of High-Quality Dragon Fruits Among Ripe Fruits The increasing demand for high-quality dragon fruit in the European market requires efficient quality assessment methods. This study explores a non-destructive image analysis approach for classifying ripe dragon fruits based on fruit ripeness and weight. A low-cost system equipped with visible and ultraviolet lighting was employed to capture images of two sets of samples of 60 and 92 ripe dragon fruits, extracting non-destructive parameters such as visible and ultraviolet perimeter, maximum and minimum diameter and area, and RGB color coordinates. Fruit destructive characterization parameters were also measured. The first set of samples was used to develop a discriminant classification model. In a first step, the main characterization magnitudes were confirmed. A ripening index was calculated based on soluble solid content and acidity. Then, a cluster analysis was used to segregate the fruits into three quality characteristics based on the ripening index and weight. In a second step, a step-by-step discriminant analysis was conducted to classify the fruits into the three quality categories (based on the laboratory-measured weight, soluble solid content and total acidity) using the non-destructive magnitudes extracted from the image analysis. The proposed classification system achieved an accuracy of nearly 85% of well-classified dragon fruits, effectively segregating dragon fruits into the three established categories. Furthermore, the established model could select the very high-quality dragon fruit (riper and larger fruits) with 93% of correctly identified products. A comparable procedure was subsequently applied to the additional set of samples (set 2), obtaining consistent results and confirming that image analysis magnitudes related to size and color enable fruit classification into the predefined weight- and ripeness-based categories. Compared to conventional destructive methods, this non-destructive approach offers a promising, cost-effective, and reliable solution for quality assessment. The findings highlight the potential for integrating smart technologies into fruit classification processes, during automatic harvest and postharvest operations, ultimately improving efficiency, reducing labor costs, and enhancing product consistency in the dragon fruit industry. Ripeness ; Ripening ; RGB color model ; Linear discriminant analysis ; Pattern recognition (psychology) ; Hue ; Quality (philosophy) Spectroscopy and Chemometric Analyses ; Botanical Research and Applications ; Food Drying and Modeling
Authors: Olivia H. Margoto | Mohammad Amin Batouei | Victor Yang | Yasmine Abdin | Abbas S. Milani
Source: Materials science forum (article, 2026-04-10)
Field: Materials Science — Polymers and Plastics

Machine Learning-Based Prediction of Forming-Induced Defects in Flax Woven Fabrics Using 3D Scanning and Haralick Texture Features Natural fiber-reinforced composites offer lightweight and sustainable alternatives for automotive and aerospace applications. However, similar to synthetic composite options, forming-induced defects such as wrinkles can reduce targeted performance. Predicting these defects is particularly challenging for natural fiber reinforcements due to the inherent variability in the fiber geometry and ensuing fabric properties. This study applies a machine learning approach to predict formability in flax woven fabrics (2×2 twill and biaxial non-crimp) using a glass fabric as a synthetic fabric reference. The fabrics’ shear, bending, tensile, and friction behaviors were experimentally characterized to capture forming-relevant mechanical properties. The fabrics were subsequently formed in single-, dual-, and triple-layer configurations over a square tool, followed by 3D scanning to quantify wrinkle distributions. Forming-induced surface deformations were transformed into grayscale maps, from which Haralick texture features were extracted. Combined with the fabric design parameters such as weave, orientation, number of layers, grammage, and thickness, the mechanical properties features were used to train linear regression models, reliably predicting select Haralick features, cross-validated using Monte Carlo simulations. Results showed that flax twill reinforcements exhibited the lowest formability, while the glass fabric formed smoothly, and biaxial non-woven fabric showed primarily localized folds. Increasing the fabric orientation from 0° to 45° improved forming performance for most woven reinforcements; but not the biaxial non-woven alternative. Linear regression models accurately predicted the defect severity via homogeneity (R² = 0.81) and dissimilarity (R² = 0.73) texture features, demonstrating that integrating texture-based image analysis with fabric parameters and mechanical properties provides a promising machine-learning-based framework for predicting surface quality upon forming of fabric-reinforced composites. Woven fabric ; Wrinkle ; Formability ; Glass fiber ; Surface finish ; Composite number ; Yarn ; Texture (cosmology) ; Surface roughness Textile materials and evaluations ; Mechanical Behavior of Composites ; Natural Fiber Reinforced Composites
Authors: Katika Harikrishna
Source: Springer Link (Chiba Institute of Technology) (article, 2026-01-07)
Field: Materials Science — Materials Chemistry

Comparative analysis of microstructural segmentation techniques on SEM and optical micrographs Since grain morphology, distribution, and interfaces have significant effects on mechanical and functional properties, accurate microstructural characterization is still indispensable to materials research. The segmentation of Al+SiC nanocomposites using backscattered electron SEM images and the segmentation of Al-Zn-Mg powder metallurgy alloys using optical micrographs are compared in this paper. This comparison, rather than reporting material-specific results, attempts to present a broader methodological perspective by trying to distill general insights into how various imaging modalities drive algorithmic choices. These accuracy aspects are not revisited here because both have already shown high accuracy in automated segmentation and grain boundary detection. Rather, focus is on how image modality, preprocessing specifications, segmentation pipelines, and post-processing techniques interact to determine the robustness, flexibility, and possible transferability of automated microstructural analysis workflows. Segmentation ; Preprocessor ; Image segmentation ; Focus (optics) ; Image processing ; Perspective (graphical) ; Scale-space segmentation ; Pattern recognition (psychology) Microstructure and mechanical properties ; Machine Learning in Materials Science ; Aluminum Alloys Composites Properties
Authors: Yongshun Wei | Guiqing Xi | Jinming Liu | Yuhao Lu | Chong Tan | Changan Xu | Weite Li
Source: Molecules (article, 2026-03-26)
Field: Chemistry — Analytical Chemistry

Key Indicator Detection and Authenticity Identification of Beer Based on Near-Infrared Spectroscopy Combined with Multi-Task Feature Extraction To address traditional beer detection limitations, this study proposes a rapid NIRS-based method for detecting key indicators and verifying authenticity. Designing Single-task (STL) and Multi-task learning (MTL) strategies, it employs Variable Importance in Projection for wavelength selection. Deep spectral features were extracted utilizing a Multi-Head Attention (MHA)-fused Convolutional Neural Network (CNN-MHA), Long Short-Term Memory (LSTM-MHA), and hybrid CNN-LSTM-MHA networks. To further enhance model performance, the Bayesian Optimization Algorithm globally optimized network hyperparameters in STL, alongside hyperparameters and multi-task loss weights in MTL. Partial least squares regression, support vector machine regression, and partial least squares discriminant analysis models were established using these features. Results indicate that the MTL-based CNN-LSTM-MHA network effectively learns shared features across multiple tasks, significantly improving model generalization. Specifically, the coefficients of determination (R2) for alcohol content and original wort concentration in the validation set were 0.996 and 0.997, respectively, with relative root mean square errors (rRMSE) of 2.024% and 2.515%. In the independent test set, the R2 were 0.995 and 0.991, with rRMSE of 2.515% and 2.087%, respectively. Furthermore, 100% classification accuracy was achieved across all datasets. This method provides an efficient technical solution for beer market regulation and real-time detection in production processes. Partial least squares regression ; Hyperparameter ; Pattern recognition (psychology) ; Convolutional neural network ; Linear discriminant analysis ; Projection (relational algebra) ; Bayesian network ; Artificial neural network ; Key (lock) Spectroscopy and Chemometric Analyses ; Fermentation and Sensory Analysis ; Advanced Chemical Sensor Technologies
Authors: Katika Harikrishna
Source: EPJ Web of Conferences (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

Comparative analysis of microstructural segmentation techniques on SEM and optical micrographs Since grain morphology, distribution, and interfaces have significant effects on mechanical and functional properties, accurate microstructural characterization is still indispensable to materials research. The segmentation of Al+SiC nanocomposites using backscattered electron SEM images and the segmentation of Al-Zn-Mg powder metallurgy alloys using optical micrographs are compared in this paper. This comparison, rather than reporting material-specific results, attempts to present a broader methodological perspective by trying to distill general insights into how various imaging modalities drive algorithmic choices. These accuracy aspects are not revisited here because both have already shown high accuracy in automated segmentation and grain boundary detection. Rather, focus is on how image modality, preprocessing specifications, segmentation pipelines, and post-processing techniques interact to determine the robustness, flexibility, and possible transferability of automated microstructural analysis workflows. Segmentation ; Preprocessor ; Focus (optics) ; Image segmentation ; Perspective (graphical) ; Image processing ; Pattern recognition (psychology) ; Electron micrographs Microstructure and mechanical properties ; Machine Learning in Materials Science ; Aluminum Alloys Composites Properties
Authors: Tiara Dia Amanda | M Lutfi Firdaus | Euis Nursaadah | Nurhamidah Nurhamidah | A Ruyani
Source: AcTion Aceh Nutrition Journal (article, 2026-03-16)
Field: Chemistry — Organic Chemistry

Effect of high temperature heating on free fatty acid content and peroxide number of packaged and bulk palm cooking oil Palm cooking oil is widely used in food processing, particularly for high-temperature frying. Although the thermal degradation of palm oil has been extensively studied, no previous study has specifically compared the degradation rates of bulk and packaged palm oil under extreme heating conditions. Repeated use at high temperatures may induce chemical changes that affect food safety and public health. This study aimed to analyze the effect of heating on changes in free fatty acid (FFA) levels and peroxide value (PV) in palm cooking oil and assess their implications for food quality. An experimental study was conducted in January 2026 at the Chemistry Education Laboratory, University of Bengkulu, using a factorial design with two main factors: oil type (packaged and bulk) and heating temperature (100, 180, and 200°C). Six repetitions were performed at 10-minute intervals over a total heating duration of 60 min. Data were analyzed using the Shapiro–Wilk normality test and two-way ANOVA in SPSS (version 25). At 200°C after 60 min, the packaged oil showed an increase in FFA to 0.31% and PV to 13.6 meqO₂/kg. Bulk oil exhibited more severe deterioration, with FFA and PV reaching 0.37% and 19 meqO₂/kg, respectively. Temperature significantly affected the increase in FFA (p = 0.000) and PV (p = 0.004) values. In conclusion, both parameters exceeded the national safety standards (SNI) threshold. Limiting the repeated use of heated palm oil is essential for protecting consumer health. Cooking oil ; Peroxide value ; Palm oil ; Factorial experiment ; Palm ; Fatty acid ; Limiting ; Acid value ; Vegetable oil Edible Oils Quality and Analysis ; Coconut Research and Applications ; Oil Palm Production and Sustainability
Authors: Lei Zeng | Guanghui Lin | Xin He | Jian Qiu | Yoon Soo Kim | Di Liang | Jialin Wei | Minh Mẫn Mai | Jingran Gao
Source: Molecules (article, 2026-03-30)
Field: Chemistry — Organic Chemistry

Geographical Authentication of Aquilaria sinensis Using Integrated C and O Stable Isotope Analysis Coupled with Chemometric Profiling Multivariate carbon and oxygen stable isotope analyses combined with chemometric methods were employed to investigate Aquilaria sinensis samples collected from six major regions in China (Honghe Hani and Yi Autonomous Prefecture and Xishuangbanna Dai Autonomous Prefecture in Yunnan Province; Zhongshan City and Maoming City in Guangdong Province; and Danzhou City and Chengmai County in Hainan Province). Isotopic δ-values were analyzed across different wood parts (longitudinal and north–south orientations), chemical fractions (de-extracted wood and α-cellulose), and geographical origins. Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), Support Vector Machine (SVM), Decision Tree, and Random Forest were applied to screen and classify the samples. Four discriminant models were successfully established, achieving a maximum accuracy of 85.7% for distinguishing Aquilaria sinensis from different regions, and 88.1% for discrimination at the provincial level. These results demonstrate that stable isotope signatures, when combined with chemometrics, provide a reliable technical approach for the traceability of incense wood and offer a reference framework for verifying the authenticity of Agarwood and related plant-derived materials. Linear discriminant analysis ; Principal component analysis ; Agarwood ; Isotope analysis ; Random forest ; Stable isotope ratio ; Chemometrics Wood and Agarwood Research ; Isotope Analysis in Ecology ; Wood Treatment and Properties
Authors: Marcin Szczepański | Ahmed Manguri
Source: Scientific Reports (article, 2026-03-21)
Field: Materials Science — Biomaterials

Recycling paper waste into structural cellulose composites with enhanced mechanical and thermal performance Recycling post-consumer cellulose waste for use in construction materials has largely been limited to board-type composite materials. While the wood-panel market is dominated by OSB and MDF, there is limited research concerning structural boards produced directly from recycled paper waste through simple processing methods. This work examines the potential to produce rigid polyurethane-cellulose composite boards using ground waste newspapers as a filler material without a catalyst. Composite boards containing 10-50 wt.% cellulose were manufactured by hot pressing and evaluated for tensile and compressive strengths, Charpy impact testing, thermogravimetric analysis, dynamic mechanical analysis, and water vapour permeability. The modulus of elasticity increased nearly three times from 10% cellulose content to 50% cellulose content due to the increased cellulose content. The maximum compressive strength was achieved at 30% cellulose content. Impact strength was not significantly impacted by filler content, resulting in consistent values for all compositions. Thermogravimetric analysis indicated that, with increasing cellulose fraction, thermal stability improved (e.g., the peak decomposition temperature (PDT) increased). Dynamic mechanical analysis results confirmed that, for boards with high cellulose content, stiffness and energy dissipation increased with each incremental increase in cellulose content. The results of the study indicate that paper recycling waste can be processed into a mechanically stable material, Polyurethane-Cellulose Boards, for use in many non-load-bearing applications within the construction sector. Using these boards represents a viable and scalable means of converting cellulose waste into a usable product in the construction industry. Cellulose ; Thermogravimetric analysis ; Ultimate tensile strength ; Composite number ; Thermal stability ; Young's modulus ; Compressive strength ; Dynamic mechanical analysis Advanced Cellulose Research Studies ; Natural Fiber Reinforced Composites ; Material Properties and Processing
Authors: Bekir Yavuzer
Source: Crystals (article, 2026-01-28)
Field: Materials Science — Biomaterials

Experimental Investigation of the Effects of Silver and Copper Content on the Fluidity of Biodegradable Zinc Alloys Ag and Cu in biodegradable Zn alloys have been the focus of research due to their biocompatible corrosion products, as well as their ability to improve the mechanical properties of the alloy. In this research, the impact of Ag and Cu on the fluidity of biodegradable Zn alloys was evaluated through the spiral fluidity test. Zn–xAg and Zn–xCu alloys containing Ag or Cu in pure Zn at proportions of 0.5, 1, 2, and 3 wt.% were prepared. In the first stage of the study, the casting temperature to be used in the fluidity tests of the alloys was determined by casting pure Zn at different temperatures. Spiral castings of the alloys were then produced and the fluidity lengths in the spiral channel were measured. Test results showed that the mold filling distances decreased with increasing amounts of Ag and Cu, with Cu causing a stronger reduction than Ag at comparable addition levels. When the Ag content in Zn was raised from 0.5 wt.% to 1 wt.%, a significant reduction in fluidity was observed. Formation of CuZn5 and ε–AgZn3 phases in the microstructures was identified as the main factor limiting melt flow. These findings provide insights into how Ag and Cu additions influence the castability of Zn alloys, offering guidance for optimizing alloy composition for biodegradable implant applications. Castability ; Alloy ; Zinc ; Copper ; Casting ; Microstructure Magnesium Alloys: Properties and Applications ; Electronic Packaging and Soldering Technologies ; Electrodeposition and Electroless Coatings