jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Yifei Peng | Jun Sun | Zhen-Yi Cai | Lei Shi | Xiaohong Wu | Chunxia Dai | Yubin Xie
Source: Horticulturae (article, 2025-07-16)
Field: Chemistry — Analytical Chemistry

Copper Stress Levels Classification in Oilseed Rape Using Deep Residual Networks and Hyperspectral False-Color Images In recent years, heavy metal contamination in agricultural products has become a growing concern in the field of food safety. Copper (Cu) stress in crops not only leads to significant reductions in both yield and quality but also poses potential health risks to humans. This study proposes an efficient and precise non-destructive detection method for Cu stress in oilseed rape, which is based on hyperspectral false-color image construction using principal component analysis (PCA). By comprehensively capturing the spectral representation of oilseed rape plants, both the one-dimensional (1D) spectral sequence and spatial image data were utilized for multi-class classification. The classification performance of models based on 1D spectral sequences was compared from two perspectives: first, between machine learning and deep learning methods (best accuracy: 93.49% vs. 96.69%); and second, between shallow and deep convolutional neural networks (CNNs) (best accuracy: 95.15% vs. 96.69%). For spatial image data, deep residual networks were employed to evaluate the effectiveness of visible-light and false-color images. The RegNet architecture was chosen for its flexible parameterization and proven effectiveness in extracting multi-scale features from hyperspectral false-color images. This flexibility enabled RegNetX-6.4GF to achieve optimal performance on the dataset constructed from three types of false-color images, with the model reaching a Macro-Precision, Macro-Recall, Macro-F1, and Accuracy of 98.17%, 98.15%, 98.15%, and 98.15%, respectively. Furthermore, Grad-CAM visualizations revealed that latent physiological changes in plants under heavy metal stress guided feature learning within CNNs, and demonstrated the effectiveness of false-color image construction in extracting discriminative features. Overall, the proposed technique can be integrated into portable hyperspectral imaging devices, enabling real-time and non-destructive detection of heavy metal stress in modern agricultural practices. Artificial intelligence ; Hyperspectral imaging ; Pattern recognition (psychology) ; Convolutional neural network ; Residual ; Computer science ; Principal component analysis ; Deep learning ; Feature (linguistics) ; Mathematics Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Date Palm Research Studies
Authors: Alannah S. Chalkley | M. López | Margaritha M. Mysior | Madeleen C. Brink | Suainibhe Kelly | Jeremy C. Simpson
Source: Frontiers in Bioengineering and Biotechnology (review, 2025-07-07)
Field: Materials Science — Biomaterials

Harnessing 3D cell models and high-resolution imaging to unveil the mechanisms of nanoparticle-mediated drug delivery Nanoparticles and nanosized materials offer huge potential in the field of drug delivery. One key aspect that dictates their successful development is the need to understand how they interact with cells at both the macro and molecular level. Delineating such interactions is vital if nanomaterials are to be targeted not only to particular organs and tissues, but also to individual cell types and ultimately specific subcellular locations. In this regard, the development of appropriate <i>in vitro</i> cell models is an essential prerequisite before animal and human trials. In recent years, as the methodology for their growth has been refined, there has been a huge expansion in the use of pre-clinical 3D cell culture models, particularly spheroids and organoids. These models are attractive because they can be combined with high-resolution fluorescence imaging to provide real-time information on how nanomaterials interact with cells. Confocal fluorescence microscopy and its associated modalities, along with high-content screening and analysis, are powerful techniques that allow researchers the possibility of extracting spatial and temporal information at multiple levels from cells and entire 3D assemblies. In this review, we summarise the state of this field, paying particular emphasis to how imaging of such models is now beginning to provide rich quantitative data about nanomaterial entry and trafficking in cells growing in 3D. We also offer a perspective on the challenges faced by such approaches, and the important questions that the drug delivery field still needs to address. Nanotechnology ; Drug delivery ; Computer science ; Drug discovery ; High-content screening ; Drug development ; Computational model ; Computational biology ; Data science ; Cell ; Bioinformatics ; Biology ; Drug ; Artificial intelligence ; Materials science Nanoparticle-Based Drug Delivery ; 3D Printing in Biomedical Research ; Graphene and Nanomaterials Applications
Authors: Christian Ebere Enyoh | Qingyue Wang | Prosper Eguono Ovuoraye | Senlin Lü | Titus Chinedu Egbosiuba
Source: Journal of Engineering and Technological Sciences (article, 2025-01-10)
Field: Materials Science — General Materials Science

Perfluorooctanesulfonic acid Sorption onto Polyethylene Microplastics: A Simulation-Driven Response Surface Optimization via Central Composite Design This study demonstrates the application of response surface modeling within a Central Composite Design (CCD) to optimize the sorption processes of perfluorooctanesulfonic acid (PFOS) onto polyethylene microplastics (PE MPs), using simulation-based data from Grand Canonical Monte Carlo (GCMC) and molecular dynamics (MD) simulations. The investigation intricately analyzes the complex interactions governing sorption phenomena (i.e. temperature and PFOS loading counts) with responses such as sorption energy, sorption density, and binding energy, employing RSM within a CCD framework. The optimization process established that the PE MPs sorption energy and sorption density were significantly dependent on PFOS loading counts in an aqueous environment. The result provides that at elevated temperature binding energy of PE MPs to Per- and polyfluoroalkyl substances was also dependent on PFOS loading counts due to associated low sensitivity temperature in the system. Optimal conditions are unveiled, enhancing sorption energy by -181.89 Kcal/mol, binding energy by -161.02 Kcal/mol, and sorption density by 1.42 g/cm³. The interaction of temperature and PFOS loading count is thoroughly examined, revealing their respective influences on sorption dynamics. These findings significantly advance our comprehension of PFOS sorption processes, fostering improved strategies for environmental remediation involving microplastic-driven sorption phenomena. Sorption ; Microplastics ; Central composite design ; Composite number ; Response surface methodology ; Polyethylene ; Environmental chemistry ; Environmental science ; Chemical engineering ; Materials science ; Environmental engineering ; Chemistry ; Composite material ; Adsorption ; Chromatography ; Organic chemistry ; Engineering Material Properties and Applications ; Synthesis and properties of polymers
Authors: Longze Li | John W. Merickel | Yalei Tang | Rongjie Song | Joshua Rittenhouse | Aleksandar Vakanski | Fei Xu
Source: Scientific Data (article, 2025-01-11)
Field: Materials Science — Materials Chemistry

Dataset of tensile properties for sub-sized specimens of nuclear structural materials Mechanical testing with sub-sized specimens plays an important role in the nuclear industry, facilitating tests in confined experimental spaces with lower irradiation levels and accelerating the qualification of new materials. The reduced size of specimens results in different material behavior at the microscale, mesoscale, and macroscale, in comparison to standard-sized specimens, which is referred to as the "specimen size effect." Although analytical models have been proposed to correlate the properties of sub-sized specimens to standard-sized specimens, these models lack broad applicability across different materials and testing conditions. The objective of this study is to create the first large public dataset of tensile properties for sub-sized specimens used in nuclear structural materials. We performed an extensive literature review of relevant publications and extracted over 1,000 tensile testing records comprising 55 columns including material type and composition, manufacturing information, irradiation conditions, specimen dimensions, and tensile properties. The dataset can serve as a valuable resource to investigate the specimen size effect and develop computational methods to correlate the tensile properties of sub-sized specimens. Ultimate tensile strength ; Microscale chemistry ; Materials science ; Tensile testing ; Composite material ; Material properties ; Mathematics Nuclear Materials and Properties ; Graphite, nuclear technology, radiation studies ; Fusion materials and technologies
Authors: Takuma Miyamura | Joël Schlecht | Oliver Dumele∞
Source: Angewandte Chemie International Edition (review, 2025-08-22)
Field: Materials Science — Materials Chemistry

Organic Spin‐State Photoswitches Organic spin-state photoswitches represent an emerging class of molecular systems capable of reversibly modulating electronic spin states using light. This review provides a comprehensive overview of the fundamental mechanisms underlying two principal switching modes: photoconformational and photochemical. Emphasis is placed on their structural design, magnetic behavior, and the methods used for their characterization, including electron paramagnetic resonance (EPR) and UV-vis spectroscopy. Selected examples illustrate the ability of these systems to control spin-polarized states, alter magnetic exchange interactions, or switch between singlet and triplet spin states upon photoirradiation. The review also discusses challenges related to thermal stability, bistability, and operation under ambient conditions. Emerging strategies for integrating photoswitches with persistent radicals or redox-active units are highlighted for their potential in molecular electronics and quantum information science. Notably, light-induced diradical species in such systems are gaining recognition as molecular qubits, offering long coherence times and spectroscopic addressability essential for quantum gate operations. By exploring design principles and technological implications, this review underscores the unique position of all-organic spin-state photoswitches at the intersection of photochemistry, magnetism, and quantum materials development. Diradical ; Bistability ; Spin engineering ; Spin states ; Spin (aerodynamics) ; Chemistry ; Quantum computer ; Nanotechnology ; Quantum ; Chemical physics ; Singlet state ; Materials science ; Physics ; Optoelectronics ; Electron ; Spin polarization ; Quantum mechanics ; Excited state Porphyrin and Phthalocyanine Chemistry ; Photochromic and Fluorescence Chemistry ; Molecular Junctions and Nanostructures
Authors: Zhixuan Zhang | Zongyao Zhang | Guorui Chang | Na Li | Weili Wang | Bingbing Fan | Weibin Zhang
Source: Journal of Advanced Ceramics (article, 2025-10-07)
Field: Materials Science — Ceramics and Composites

Nitrogen-induced stacking fault energy reduction enables spinodal-decomposition-driven toughening in (Ti,Zr)C ceramics Transition metal carbides exhibit outstanding mechanical properties but suffer from a critical hardness-toughness trade-off. Spinodal decomposition-mediated phase separation, induced by high-temperature aging, is an effective strategy to enhance the mechanical properties of carbide ceramics. However, the typically high stacking fault energy in carbide ceramics restricts the dislocation pinning effects of spinodal decomposition interfaces, hampering potential hardness and toughness improvements. Guided by first-principles calculations, this study employs (Ti, Zr)C carbide ceramics as a representative system and systematically lowers its stacking fault energy through nitrogen (N) incorporation. With optimized composition and controlled aging, distinct stacking faults emerged after short-term aging. As aging time increased, these stacking faults progressively transformed into dislocation sources, facilitating dislocation multiplication. Mechanical testing revealed that samples incorporating 25% N followed by aging exhibited significant enhancements: hardness and fracture toughness increased by approximately 40% and 50%, respectively, compared to the initial material. However, at higher N concentrations, excessive elastic strain energy accumulation induced lamellar thickening, diminishing the extent of improvement in hardness and toughness. This work designs a strategy to lower stacking fault energy in carbide ceramics, overcoming its constraint on performance enhancement via spinodal decomposition and enabling hardness-toughness synergy via spinodal decomposition through theoretical and processing solutions. Stacking-fault energy ; Spinodal decomposition ; Carbide ; Ceramic ; Fracture toughness ; Dislocation ; Stacking ; Toughness Advanced ceramic materials synthesis ; Advanced materials and composites ; Aluminum Alloys Composites Properties
Authors: András Füredi | Szilárd Tóth | Kristóf Hegedüs | Pál Szabó | Anikó Gaál | Gergő Barta | Lívia Naszályi Nagy | Krisztina Kiss | Kata Bölcskei | Zoltán Szeltner | Eszter Bajtai | Balázs Gombos | Dániel Kiss | Mihály Cserepes | Attila Kiss | Péter Pokreisz | Lukas Kenner | Sandra Högler | Csaba Magyar | James Cowles | Agnes Csiszar | József Tóvári | Dávid Szüts | Zsuzsanna Helyes | Zoltán Varga | Gábor Mező | Gergely Szakács
Source: Molecular Cancer (article, 2025-10-27)
Field: Materials Science — Biomaterials

Safe delivery of a highly toxic anthracycline derivative through liposomal nanoformulation achieves complete cancer regression Liposomal formulations of extremely cytotoxic anthracycline analogs, such as LiPyDau, represent a promising and highly effective therapeutic approach for combating drug resistant cancer. Anthracycline ; Liposome ; Drug ; Cytotoxic T cell ; Cytotoxicity ; Doxorubicin ; Derivative (finance) Nanoparticle-Based Drug Delivery ; Nanoplatforms for cancer theranostics ; RNA Interference and Gene Delivery
Authors: Wioletta Olejarz | Karol Sadowski | Piotr Roszkowski | Anna Bielenica | Marek Wiśniewski | Marta Struga | Daniel Szulczyk
Source: Scientific Reports (article, 2025-05-21)
Field: Chemistry — Organic Chemistry

Design and in vitro evaluation of novel tetrazole derivatives of dianisidine as anticancer agents targeting Bcl-2 apoptosis regulator This study examines the synthesis and biological evaluation of novel tetrazole derivatives of 3,3'-dimethoxybenzidine as potential anticancer agents, focusing on their cytotoxic, apoptotic, and anti-inflammatory properties. Ten derivatives were synthesized using thioureas as precursors, characterized through spectroscopic methods, and assessed for their in silico toxicological profiles using the ADMET-AI and ProTox 3.0 platforms. In vitro cytotoxic activity was evaluated against four human cancer cell lines (HTB-140, A549, HeLa, SW620) and one normal cell line (HaCaT) using MTT and LDH assays. Mechanistic studies included apoptosis assessment via flow cytometry and interleukin-6 (IL-6) analysis using ELISA. The synthesized tetrazole derivatives demonstrated significant anticancer potential, exhibiting selective cytotoxicity against cancer cell lines, robust induction of apoptosis, and a notable reduction in IL-6 levels. Their favorable toxicity profiles, as observed in both in silico and in vitro evaluations, support their potential as promising candidates for further development. The tested compounds showed strong inhibitory activity against the apoptosis regulator Bcl-2, with binding affinities comparable to those of native ligands. Western blot analysis revealed a dramatic loss of Bcl-2 protein expression in selected cancer cells during exposure to compound 5. Additionally, this research highlights the innovative use of hazardous substrates in drug discovery, aligning with the principles of green chemistry. Future efforts should focus on optimizing the most active derivatives and conducting in vivo studies to confirm their therapeutic potential and safety. Regulator ; Apoptosis ; In vitro ; Tetrazole ; Chemistry ; Pharmacology ; Cancer research ; Biology ; Biochemistry ; Stereochemistry Synthesis of Tetrazole Derivatives ; Synthesis and Characterization of Heterocyclic Compounds ; Synthesis and biological activity
Authors: Van T. Nguyen | Trung Hiếu Vũ | Phuong Thy Nguyen | Moon Il Kim
Source: Biosensors (article, 2025-08-13)
Field: Materials Science — Materials Chemistry

Dual-Mode Optical Detection of Sulfide Ions Using Copper-Anchored Nitrogen-Doped Graphene Quantum Dot Nanozymes We present a dual-mode optical sensing strategy for selective and sensitive detection of sulfide ions (S<sup>2-</sup>), employing copper-anchored nitrogen-doped graphene quantum dots (Cu@N-GQDs) as bifunctional nanozymes. The Cu@N-GQDs were synthesized via citric acid pyrolysis in the presence of ammonium hydroxide (serving as both nitrogen source and reductant) and copper chloride, leading to uniform incorporation of copper oxide species onto the N-GQD surface. The resulting nanohybrids exhibit two synergistic functionalities: intrinsic fluorescence comparable to pristine N-GQDs, and significantly enhanced peroxidase-like catalytic activity attributed to the anchored copper species. Upon interaction with sulfide ions, the system undergoes a dual-optical response: (i) fluorescence quenching via Cu-S complexation, and (ii) inhibition of peroxidase-like activity due to the deactivation of Cu catalytic centers via the interaction with S<sup>2-</sup>. This dual-signal strategy enables sensitive quantification of S<sup>2-</sup>, achieving detection limits of 0.5 µM (fluorescence) and 3.5 µM (colorimetry). The sensor demonstrates excellent selectivity over competing substances and high reliability and precision in real tap water samples. These findings highlight the potential of Cu@N-GQDs as robust, bifunctional, and field-deployable nanozyme probes for environmental and biomedical sulfide ion monitoring. Graphene ; Bifunctional ; Copper ; Chemistry ; Inorganic chemistry ; Quantum dot ; Sulfide ; Fluorescence ; Catalysis ; Copper sulfide ; Photochemistry ; Materials science ; Nanotechnology ; Organic chemistry Advanced Nanomaterials in Catalysis ; Carbon and Quantum Dots Applications ; Nanocluster Synthesis and Applications
Authors: Kaixuan Li | Yijun Guo | Hong Zhong | Ying Jin | Bin Li | Huimin Fang | Lijian Yao | Chao Zhao
Source: Sensors (article, 2025-09-09)
Field: Chemistry — Analytical Chemistry

Rapid Identification of Dendrobium Species Using Near-Infrared Hyperspectral Imaging Technology <i>Dendrobium officinale</i> is a valuable Chinese medicinal herb, but distinguishing it from other Dendrobium species after processing is challenging, leading to low classification accuracy and time-consuming analysis. This study proposes a rapid classification model based on near-infrared hyperspectral imaging (NIR-HSI), incorporating data preprocessing and feature wavelength selection. Five Dendrobium species-<i>D. officinale</i>, <i>D. aphyllum</i>, <i>D. chrysanthum</i>, <i>D. fimbriatum</i>, and <i>D. thyrsiflorum</i>-were used. Spectral preprocessing techniques like normalization and smoothing were applied, and Support Vector Machine (SVM) models were constructed. Normalization improved both accuracy and stability, with the full-spectrum Normalize-SVM model achieving 97% accuracy for calibration and 88% for prediction. <i>D. chrysotoxum</i> performed best, with all metrics reaching 100%, while <i>D. aphyllum</i> had poor classification (40% recall and 51.74% F1 score). To improve efficiency and performance, feature wavelength selection was performed using Competitive Adaptive Reweighted Sampling (CARS) and Successive Projections Algorithm (SPA). The CARS-Normalize-SVM model yielded the best results: 98% accuracy for calibration and 96% for prediction, improving by 1% and 8%, respectively. D. aphyllum's classification also improved significantly, with a 100% recall rate and 95.24% F1 score. These findings highlight hyperspectral imaging's potential for rapid Dendrobium species identification, supporting future quality control and market supervision. Hyperspectral imaging ; Preprocessor ; Normalization (sociology) ; Pattern recognition (psychology) ; Smoothing ; Dendrobium ; Data pre-processing ; Precision and recall Spectroscopy and Chemometric Analyses ; Biological and pharmacological studies of plants ; Smart Agriculture and AI
Authors: Mohamed Bechir Ben Hamida | Ali Basem | Ala Eldin A. Awouda | Aman Sharma | Narinderjit Singh Sawaran Singh | Nashmi H. Alrasheedi | Komal Parashar | Loghman Mostafa
Source: Scientific Reports (article, 2025-08-28)
Field: Materials Science — Materials Chemistry

Optimizing MXene graphene based fluids for solar energy conversion and storage using a novel intelligent framework The thermophysical properties of novel Graphene/MXene-based fluids have great potential for enhancing the efficiency of solar energy systems. However, optimizing these properties remains challenging due to the complex interactions between nanomaterial composition and system conditions. This study presents a new hybrid framework that combines response surface methodology (RSM), heuristic and metaheuristic optimization, and advanced decision-making techniques to enhance the thermal conductivity (TC) and dynamic viscosity (DV) of these fluids. RSM-based predictive models demonstrated high accuracy (R² = 0.9997 for TC and 0.9984 for DV), validated using regression graphs, violin plots, and absolute relative deviation analysis. Optimization was conducted using enhanced hill climbing (EHC), NSGA-II, and multi-objective ant lion optimizer (MOALO), with decision-making strategies such as desirability function and VIKOR technique. Results revealed that optimal MXene ratios depend on nanomaterial mass fraction (MF) and temperature, with optimal conditions clustering around 60 °C, MF of 1.5-2 wt%, and MXene ratios of 0.47-0.5. Decision-making analysis highlighted the trade-offs between TC and DV based on varying weight distributions. This research provides a cost-effective methodology to optimize nanofluids for solar energy applications with high precision, reducing computational and laboratory costs. Graphene ; Computer science ; Energy storage ; Materials science ; Solar energy conversion ; Solar energy ; Process engineering ; Nanotechnology ; Physics ; Electrical engineering ; Engineering MXene and MAX Phase Materials ; Energy Harvesting in Wireless Networks ; Advanced Memory and Neural Computing