jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Beom‐Jin Lee | Manal Hadi Ghaffoori Kanaan | Sura Saad Abdullah | Abdolmajid Ghasemian
Source: OBM Genetics (review, 2025-08-18)
Field: Materials Science — Materials Chemistry

Plant-Derived Nanoparticles in Cancer Therapy: A Comprehensive Review of Recent Advances and Future Prospects Cancer continues to be one of the leading causes of global death, and conventional therapies have limited efficacy because of their toxicity, drug resistance, and off-target effects. Plant-derived nanoparticles (PDNPs) have emerged as suitable alternatives as they have biocompatibility, biodegradability, and multifunctional therapy. In this review, we discussed the recent advancements in PDNPs for cancer therapy, including the green synthesis of PDNPs using phytochemical (flavonoids, terpenoids) reducing and capping agents, subsequent physicochemical characterization, and mechanisms of action. PDNPs take advantage of passive targeting via the enhanced permeability and retention (EPR) effect, and active targeting through ligand-receptor targeting (folate, estrogen receptors). PDNPs also utilize features of the tumor microenvironment (TME) (acidic pH, redox imbalance, protease overexpression, etc.) to mediate stimuli-responsive drug release. PDNPs have potent anticancer activity by inducing apoptosis via ROS generation and mitochondrial dysfunction, regulating immune responses (repolarizing tumor-associated macrophages), and reducing metastasis by inhibiting epithelial-mesenchymal transition (EMT). Various PDNP platforms from metallic nanoparticles (Au, Ag, ZnO), to plant virus nanoparticles (TMV, CPMV), to polymeric/lipid carriers promote effective delivery of chemotherapeutics, phytocompounds (curcumin, quercetin), and gene-editing technologies (CRISPR/Cas9). Despite their better biosafety and selective cytotoxicity, challenges remain with scalability, pharmacokinetics, and long-term toxicity, and all these will need to be further addressed. Future opportunities are anticipated in AI-assisted design, utilizing CRISPR integration for precision gene editing, and developing tailored PDNP formulation strategies unique to the tumor's molecular profile (precision medicine). PDNPs would be a disruptive, sustainable delivery vehicle for conventional therapies and represent a transformative surface-initiated approach that would certainly advance cancer nanomedicine and could symbolize the necessary shift in the current paradigm. Cancer therapy ; Cancer ; Nanotechnology ; Medicine ; Intensive care medicine ; Materials science ; Internal medicine Nanoparticles: synthesis and applications ; Curcumin's Biomedical Applications ; Medicinal Plant Research
Authors: Usama Akram Saed | Alaa H. Ali | Ammar Saoud | Zeyad Zeitoun
Source: BULLETIN OF CHEMICAL REACTION ENGINEERING AND CATALYSIS (article, 2025-10-07)
Field: Chemistry — Organic Chemistry

Enhanced Adsorption of Brilliant Green Dye Using Barium Ferrite/Graphene Oxide Nanocomposites This study presents the synthesis and characterization of barium ferrite/graphene oxide (BaFeO/GO) nanocomposites for the adsorption of brilliant green dye (BGD) from aqueous solutions. BaFeO/GO nanocomposites were fabricated via a co-precipitation method with varying GO content (10-30 wt%), and characterized using Fourier Transform Infra rEd (FTIR), X-Ray Diffraction (XRD), Scanning Electron Microscope (SEM), Field-Emission Scanning Electron Microscopy (FESEM), Transmission Electron Microscope (TEM), Brunauer, Emmett, and Teller (BET), and Vibrating Sample Magnetometer (VSM) techniques. The incorporation of GO enhanced the surface area, reduced BaFeO nanoparticle agglomeration, and introduced additional oxygen-containing functional groups, significantly improving the adsorption performance. Batch adsorption experiments were conducted to evaluate the effects of pH, contact time, adsorbent dose, and initial dye concentration. The maximum dye removal efficiency reached 98.9% with the BaFeO/30%GO composite. Kinetic studies showed excellent agreement with the pseudo-second-order model, while adsorption isotherm analysis indicated that the Langmuir model best fit the equilibrium data, suggesting monolayer adsorption. These results demonstrate the potential of BaFeO/GO nanocomposites as efficient, magnetically separable adsorbents for the removal of cationic dyes from wastewater. Copyright © 2025 by Authors, Published by BCREC Publishing Group. This is an open access article under the CC BY-SA License (https://creativecommons.org/licenses/by-sa/4.0). Adsorption ; Nanocomposite ; Brilliant green ; Scanning electron microscope ; Langmuir adsorption model ; Monolayer ; Transmission electron microscopy ; Aqueous solution Nanomaterials for catalytic reactions ; Adsorption and biosorption for pollutant removal ; Graphene and Nanomaterials Applications
Authors: Leon Ngwenya | Musamba Banza | Tumisang Seodigeng
Source:  (article, 2025-07-29)
Field: Materials Science — Biomaterials

Adsorption of Nickel (II) from Aqueous Solution Using Recyclable Three-Dimensional Cellulose Nanocrystal Hydrogel: A Central Composite Design To remove nickel (II) from an aqueous solution, cellulose nanocrystals (CNCs) were modified as an adsorbent. The FTIR and SEM were used to characterise the properties of CNCs. In addition to how well they predicted reaction (adsorption capacity), the central composite design was used. The response surface model method performs well, according to statistical data. Four operational variables were studied: The initial concentration of the nickel (II) solution in mg/L, the pH, the contact period in minutes, and the adsorbent dose in g/100 mL. The removal percentage (%) was the result. The percentage removal was 98% after 178 min of contact, a starting concentration of 110 mg/L, an adsorbent dosage of 9.3 g, and an initial pH of 3.5. The R2 was 0.996, the adjusted R2 was 0.921, and the predicted R2 was 0.945. The quadratic equation was determined using central composite design. The FTIR examination revealed that the functional groups, hydroxyl groups (OH), peaked around 3300–3500 cm−1, and carboxyl groups (COOH) peaked around 1700 cm−1. Nanocrystal ; Nickel ; Adsorption ; Cellulose ; Aqueous solution ; Composite number ; Materials science ; Chemical engineering ; Composite material ; Nanotechnology ; Metallurgy ; Chemistry ; Organic chemistry ; Engineering Advanced Cellulose Research Studies ; Nanomaterials for catalytic reactions ; Adsorption and biosorption for pollutant removal
Authors: Sihua Zeng | Cheng Qin | Yuzhu Zhang | Haoyu Chen | Hua Lin
Source: Materials (article, 2025-11-17)
Field: Materials Science — Materials Chemistry

A Green-Synthesized Fluorescent Carbon Dot Probe Derived from Banana Peel for Cellular Imaging and Sensing of Tetracycline The valorization of biomass waste represents an important direction in green chemistry. This study successfully prepared blue fluorescent carbon dots (BP-CDs) from waste banana peels via a one-step hydrothermal method, establishing a dual-functional platform for both pollutant detection and cellular imaging. The resulting material exhibited uniform particle size (~2.05 nm), good water dispersibility, and strong fluorescence emission at 445 nm under 360 nm excitation. It maintained over 93% of its initial fluorescence intensity after 20 days, demonstrating excellent stability. Based on the inner filter effect, the probe enabled a highly selective detection of tetracycline with a detection limit of 0.191 µM and two wide linear ranges (0-15 µM, R<sup>2</sup> = 0.996; 15-95 µM, R<sup>2</sup> = 0.991). Cellular experiments confirmed the good biocompatibility of BP-CDs (cell viability > 84%) and their successful application in cell imaging. More importantly, the probe achieved visual observation and semi-quantitative analysis of the distribution and content of tetracycline in living cells, providing a direct tool for studying the cellular behavior of antibiotics. This work not only offers a new strategy for banana peel valorization but also develops a green fluorescence imaging platform suitable for tracking intracellular pollutants. Fluorescence ; Tetracycline ; Biocompatibility ; Banana peel ; Detection limit ; Carbon fibers ; Particle size Carbon and Quantum Dots Applications ; Electrochemical sensors and biosensors ; Advanced Nanomaterials in Catalysis
Authors: Seyed Saeid Tayebi | Nate Dowdall | Todd Hoare | Prashant Mhaskar
Source: Nanoscale (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Data-driven optimization of nanoparticle size using the prediction reliability enhancing parameter (PREP) The particle size of a nanoparticle plays a crucial role in regulating its biodistribution, cellular uptake, and transport mechanisms and thus its therapeutic efficacy. However, experimental methods for achieving a desired nanoparticle size and size distribution often require numerous iterations that are both time-consuming and costly. In this study, we address the critical challenge of achieving nanoparticle size control by implementing the Prediction Reliability Enhancing Parameter (PREP), a recently developed data-driven modeling-based product design approach that significantly reduces the number of experimental iterations needed to meet specific design goals. We applied PREP to effectively predict and control particle sizes of two distinct nanoparticle types with different target particle size properties: (1) thermoresponsive covalently-crosslinked microgels fabricated <i>via</i> precipitation polymerization with targeted temperature-dependent size properties and (2) physical polyelectrolyte complexes fabricated <i>via</i> charge-driven self-assembly with particle sizes and colloidal stabilities suitable for effective circulation. In both cases, PREP enabled efficient and precise size control, achieving target outcomes in only two iterations in each case. These results provide motivation to further utilize PREP in streamlining experimental workflows in various biomaterials optimization challenges. Nanoparticle ; Particle size ; Materials science ; Reliability (semiconductor) ; Nanotechnology ; Computer science ; Biodistribution ; Chemical engineering ; Chemistry ; Physics Machine Learning in Materials Science ; Machine Learning and Data Classification ; Computational Drug Discovery Methods
Authors: Di Wu | R. F. Li | Tianlong Zhang
Source: cMat. (article, 2025-12-01)
Field: Materials Science — Materials Chemistry

Design, Manufacture and Prospects of Heterostructured Materials ABSTRACT Heterostructured materials, characterized by distinct microstructures and mechanical properties across different micro‐regions, have consistently demonstrated their ability to effectively mitigate the strength‐ductility trade‐off in structural materials. Mechanisms such as geometrically necessary dislocations and heterogeneous deformation‐induced strengthening have been proposed to elucidate their strengthening and toughening processes. Despite their promising potential in theoretical studies and laboratory settings, industrial applications remain limited due to challenges such as high production costs and inadequate understanding of critical properties including fatigue resistance and corrosion behavior. This review explores manufacturing methods for heterogeneous structures, strategies for microstructure regulation, and future research directions for advancing their industrial applications. Toughening ; Microstructure ; Corrosion ; Strengthening mechanisms of materials ; Production (economics) Microstructure and mechanical properties ; Advanced ceramic materials synthesis ; MXene and MAX Phase Materials
Authors: Sumit Chauhan | Anupama Maurya | Akshay Singh | Rahul Singh | Urmila Nishad
Source: International Journal of Pharmaceutical Research and Applications (review, 2025-03-01)
Field: Materials Science — Biomaterials

Nanoliposomes for Site-Specific Drug Delivery in Cancer Therapy: A Comprehensive Review Cancer remains one of the leading causes of mortality worldwide, and conventional chemotherapy often suffers from lack of specificity, systemic toxicity, and multidrug resistance. Nanoliposomes—submicron vesicular systems composed of lipid bilayers—have emerged as a promising nanocarrier for targeted cancer therapy. This review focuses on the advancements, mechanisms, and applications of nanoliposomes for site-specific drug delivery in cancer, highlighting design strategies, targeting approaches, therapeutic efficacy, and future prospects. Cancer therapy ; Drug delivery ; Medicine ; Drug ; Targeted drug delivery ; Cancer ; Pharmacology ; Nanotechnology ; Internal medicine Nanoparticle-Based Drug Delivery ; RNA Interference and Gene Delivery ; Cancer Research and Treatment
Authors: Ruiyuan Kang | Dimitrios C. Kyritsis | Panos Liatsis
Source: PLoS ONE (article, 2025-01-24)
Field: Chemistry — Spectroscopy

Comparative analysis of data-driven models for spatially resolved thermometry using emission spectroscopy A methodology is proposed, which addresses the caveat that line-of-sight emission spectroscopy presents in that it cannot provide spatially resolved temperature measurements in non-homogeneous temperature fields. The aim of this research is to explore the use of data-driven models in measuring temperature distributions in a spatially resolved manner using emission spectroscopy data. Two categories of data-driven methods are analyzed: (i) Feature engineering and classical machine learning algorithms, and (ii) end-to-end convolutional neural networks (CNN). In total, combinations of fifteen feature groups and fifteen classical machine learning models, and eleven CNN models are considered and their performances explored. The results indicate that the combination of feature engineering and machine learning provides better performance than the direct use of CNN. Notably, feature engineering, which is comprised of physics-guided transformation, signal representation-based feature extraction and Principal Component Analysis is found to be the most effective. Moreover, it is shown that when using the extracted features, the ensemble-based, light blender learning model offers the best performance with RMSE, RE, RRMSE and R values of 64.3, 0.017, 0.025 and 0.994, respectively. The proposed method, based on feature engineering and the light blender model, is capable of measuring nonuniform temperature distributions from low-resolution spectra, even when the species concentration distribution in the gas mixtures is unknown. Artificial intelligence ; Feature (linguistics) ; Feature engineering ; Convolutional neural network ; Principal component analysis ; Pattern recognition (psychology) ; Computer science ; Feature extraction ; Spectroscopy ; Transformation (genetics) ; Biological system ; Machine learning ; Algorithm ; Deep learning ; Physics ; Chemistry Spectroscopy and Laser Applications ; Air Quality Monitoring and Forecasting ; Advanced Chemical Sensor Technologies
Authors: Yoonho Lee | Yongwon Kim | Jaekwan Lim | Un-Su Kang | Jung-Gu Kim
Source: Research Square (preprint, 2025-01-20)
Field: Materials Science — Materials Chemistry

Effects of pH, Chloride, and Temperature on Copper Corrosion in an Aerobic Deep Geological Repository through Regression and Simulation Analysis Copper ; Chloride ; Corrosion ; Regression analysis ; Regression ; Environmental science ; Metallurgy ; Materials science ; Computer science ; Statistics ; Mathematics ; Machine learning Corrosion Behavior and Inhibition ; Analytical Chemistry and Sensors ; Building materials and conservation
Authors: Ritwika Mandal | Maciej Lorenc | Marco Cammarata | Matteo Levantino | Serhane Zerdane | Étienne Janod | Laurent Cario | Hiroko Tokoro | Shin‐ichi Ohkoshi | Elżbieta Trzop | Marina Servol | Guénolé Huitric | H. Cailleau | V. Ta Phuoc | Florian Banhart | Cristian Enachescu | Laurenţiu Stoleriu | Céline Mariette
Source: Communications Materials (article, 2025-09-29)
Field: Materials Science — Materials Chemistry

Picosecond anisotropic phase separation governing photoinduced phase stability in submicron Ti3O5 crystals Abstract Recently developed ceramic material Ti 3 O 5 exhibits fascinating application properties, from ultrafast switching between metallic and insulating phases to light-to-heat conversion and storage. While the states conferring such properties are not spatially homogeneous, the origin of phase separation and the link between the phase coexistence and dynamics, key for stability of such states, is still little known. In this work, we use time-resolved X-ray diffraction and numerical simulations to establish rules by which the dynamics of heat-driven transition in laser excited Ti 3 O 5 crystallites occur in space and time. The studies are conducted on submicron Ti 3 O 5 crystallites and span a broad timescale allowing separation of phase change regimes. Our results reveal the influence of nanoscopic morphology on the mechanism of macroscopic transformation triggered by laser excitation. Crystallite ; Picosecond ; Phase (matter) ; Nanoscopic scale ; Ceramic ; Anisotropy ; Excited state Phase-change materials and chalcogenides ; Laser Material Processing Techniques ; Cultural Heritage Materials Analysis
Authors: Guocui Qi | Chengzhong Liu | Junying Han | Yuqian Zhou | Yongsheng Li | Yalong Wang
Source: Foods (article, 2025-10-18)
Field: Chemistry — Analytical Chemistry

Variety Identification of Corn Seeds Based on Hyperspectral Imaging and Residual Mamba 1D CNN Maize is a globally important crop, and reliable identification of seed varieties is vital for breeding and quality assurance. To overcome the limitations of conventional methods, this study developed a non-destructive approach integrating hyperspectral imaging (HSI) and deep learning. A Residual Mamba One-Dimensional Convolutional Neural Network (RM1DNet) is proposed, which integrates residual and Mamba modules to enhance feature learning. RM1DNet achieved 94.85% accuracy in classifying 20 maize seed varieties, outperforming traditional classifiers and baseline deep models. These results demonstrate the robustness and efficiency of RM1DNet, highlighting its potential to advance intelligent seed variety identification using hyperspectral data. Hyperspectral imaging ; Residual ; Convolutional neural network ; Robustness (evolution) ; Pattern recognition (psychology) ; Identification (biology) Spectroscopy and Chemometric Analyses ; Remote Sensing and Land Use ; Smart Agriculture and AI
Authors: Salah Elkun | Mohsen Ghali | T. Sharshar | M.M. Mosaad
Source: Scientific Reports (article, 2025-09-30)
Field: Materials Science — Materials Chemistry

Carbon quantum dots from silymarin extraction for multi-fluorescent sensors, photocatalysis, and anticancer activity applications In this work, water-soluble fluorescent carbon quantum dots were prepared by a one-stage hydrothermal method via a green synthesis route using silymarin extract from milk thistle seeds as a single precursor. Various experimental techniques were used to characterize the synthesized silymarin-based carbon quantum dots (SM-CQDs), to confirm their structure and investigate their potential applications in fluorescent sensors, photocatalysis and anticancer activity. The prepared SM-CQDs exhibited amorphous graphitic structure with a spherical shape and an average particle size of 4.6 ± 0.7 nm, as indicated by XRD, Raman, and TEM measurements. The FTIR results indicate the presence of diverse functionalities on the surface of the SM-CQDs, which were further confirmed by XPS analysis. Fluorescence characterization of the prepared SM-CQDs revealed blue emission with a fluorescence quantum yield (QY) value of 4.9%. Furthermore, the prepared dots exhibit temperature-sensitive photoluminescence (PL) behavior, which has been interestingly used to design economical, green, and highly sensitive fluorescent probes for the detection of Fe<sup>3+</sup> ions and H<sub>2</sub>O<sub>2</sub> with a detection limit (DL) of 0.071 and 0.159 µM, respectively. A nano thermosensor has been demonstrated to have a wide temperature range of 10-90 °C and a good recovery, exhibiting a thermal sensitivity of 0.8% °C<sup>- 1</sup> based on its temperature-sensitive behavior. Moreover, we demonstrated that the as-prepared SM-CQDs can serve as excellent photocatalysts for the degradation of methylene blue (MB) dye under direct visible light irradiation with a degradation efficiency of about 45% within 105 min. The obtained SM-CQDs have a zeta potential of -22.24 mV, indicating excellent stability in water. Finally the SM-CQDs exhibit anticancer activity showing cytotoxicity to Caco2 cells with IC<sub>50</sub> = 285.2 ± 11.9 µg/mL. These features of SM-CQDs indicate their potential in applications such as sensing, cell imaging, and optoelectronics. Fluorescence ; Quantum yield ; Zeta potential ; Carbon fibers ; Quantum dot ; Photoluminescence ; Detection limit ; Fourier transform infrared spectroscopy ; X-ray photoelectron spectroscopy ; Degradation (telecommunications) Carbon and Quantum Dots Applications ; Nanocluster Synthesis and Applications ; Advanced Nanomaterials in Catalysis
Authors: Temitayo S. Ogedengbe | Sunday A. Afolalu | Ting Tin Tin | Oluwaseyi Balogun | Adekunle Akanni Adeleke | Omolayo M. Ikumapayi | Seun Jesuloluwa | Adeiza Avidime Samuel
Source: International Journal of Design & Nature and Ecodynamics (review, 2025-02-28)
Field: Materials Science — Materials Chemistry

Evaluating the Efficacy of Agro-Waste Derived Flux for Enhancing the Weldability of Steel – A Review Material failure often leads to disastrous consequences, but it can often be effectively prevented.The need to prevent material failure has sparked extensive research into strengthening engineered materials, leading to diverse approaches in material enhancement.Global population growth has intensified the demand for agricultural products, leading to increased environmental pollution and degradation over time.As a result, the use of these agricultural residues as flux materials has been explored in engineering applications.This study reviewed various techniques to enhance the structural integrity of carbon steels by incorporating agricultural waste products, notably date seeds, palm kernel shells, and banana peels.A comprehensive analysis was conducted on the engineering properties of these waste materials.The findings indicate that date seeds and palm kernel shells exhibit superior reinforcement capabilities, making them more effective in enhancing the performance of various steel types compared to banana peels.Consequently, it is advisable to consider the utilization of date seeds and palm kernel shells for engineering applications. Weldability ; Flux (metallurgy) ; Environmental science ; Waste management ; Materials science ; Process engineering ; Engineering ; Metallurgy ; Welding Metal Alloys Wear and Properties ; Advanced Welding Techniques Analysis ; Microstructure and Mechanical Properties of Steels