jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Amanpreet K. Sidhu | Naveen Verma | Priya Kaushal | Sanskruthi B. Agrawal | Shally Bhatia
Source: Discover Applied Sciences (article, 2025-05-17)
Field: Materials Science — Materials Chemistry

Green synthesis of polymeric nanoparticles: agricultural applications and toxicological implications Polymeric nanoparticles (PNPs) synthesized using green methods have tremendous potential in sustainable agriculture. The article explores the fundamentals of green synthesis, which uses biopolymers including chitosan, alginate, and cellulose to produce eco-friendly and biodegradable nanoparticles. Green synthesis approaches provide a possible alternative to traditional procedures, decreasing hazardous by-products and enhancing environmental compatibility. Studies demonstrate that PNPs an encapsulate agrochemicals, enabling controlled release systems that reduce fertilizer and pesticide usage by up to 40% while improving crop yield by 20–30% under specific conditions. Additionally, PNPs improve disease resistance by delivering bioactive compounds, such as plant growth regulators, directly to target sites. Research on the interaction of PNPs with plants reveals improved absoption efficiency (up to 50% higher) and translocation within plant tissues, positively influencing plant physiology and development. Preliminary case studies suggest that while green synthesis methods are cost-effective at small scales, reducing production costs by approximately 20% compared to conventional methods, challenges remain in scaling these processes for the industrial applications. Furthermore, reports suggest potential risks, such as a 10–15% reduction in soil microbial diversity at higher doses, which makes a careful analysis of toxicological effects necessary given their growing use. This review emphasises the necessity of conducting thorough environmental impact assessments and developing safety standards for their use. Innovative research addressing the scalability and cost issues will establish the green synthesis of PNPs as a fundamental component of sustainable agricultural methods. Nanoparticle ; Nanotechnology ; Agriculture ; Materials science ; Biology ; Ecology Nanoparticles: synthesis and applications ; Microplastics and Plastic Pollution
Authors: Kai‐Hung Huang | Kitmin Chen | Nicolás M. Morato | Thomas C. Sams | Eric T. Dziekonski | R. Graham Cooks
Source: Chemical Science (article, 2025-01-01)
Field: Chemistry — Spectroscopy

High-throughput microdroplet-based synthesis using automated array-to-array transfer Automation of chemical synthesis and high-throughput (HT) screening are important for speeding up drug discovery. Here, we describe an automated HT picomole scale synthesis system which uses desorption electrospray ionization (DESI) to create microdroplets of reaction mixtures at individual positions from a two-dimensional reactant array and transfer them to a corresponding position in an array of collected reaction products. On-the-fly chemical transformations are facilitated by the reaction acceleration phenomenon in microdroplets and high reaction conversions are achieved during the milliseconds droplet flight time from the reactant to the product array. Successful functionalization of bioactive molecules is demonstrated through the generation of 172 analogs (64% success rate) using multiple reaction types. Synthesis throughput is ∼45 seconds/reaction including droplet formation, reaction, and collection steps, all of which occur in an integrated fashion, generating product amounts sufficient for subsequent bioactivity screening (low ng to low μg). Quantitative performance was validated using LC/MS. This system bridges the demonstrated capabilities of HT-DESI for reaction screening and label-free bioassays, allowing consolidation of the key early drug discovery steps around a single synthetic-analytical technology. Throughput ; Automation ; Electrospray ionization ; High-throughput screening ; Drug discovery ; Computer science ; Chemistry ; Nanotechnology ; Mass spectrometry ; Combinatorial chemistry ; Materials science ; Chromatography ; Engineering ; Telecommunications ; Biochemistry Mass Spectrometry Techniques and Applications ; Microfluidic and Capillary Electrophoresis Applications ; Innovative Microfluidic and Catalytic Techniques Innovation
Authors: Xin Kang Li | Li Tang | Ze Ying Li | Dian Qiu | Zhuo Ling Yang | Xiao Yi Zhang | Xiang‐Zhi Zhang | Jìng Guo | Bao Qiong Li
Source: npj Science of Food (article, 2025-02-05)
Field: Chemistry — Analytical Chemistry

Geographical origin discrimination of Chenpi using machine learning and enhanced mid-level data fusion Chenpi, or dried tangerine peel, is a traditional Chinese ingredient valued in medicine and edible for its digestive and respiratory benefits. The geographical origin of Chenpi is important, as it can impact its quality, active compounds and market value. This study develops a strategy to distinguish Chenpi samples on its origin. Thirty-nine samples from eight regions in Xinhui district (Guangdong, China) are analyzed by gas chromatography (GC) and mid-infrared (MIR) technique. Four machine learning methods are employed to establish discrimination models based on GC and MIR data, with two mid-level data fusion strategies to combine the data. The results show that data fusion significantly improves Chenpi origin discrimination. The K-nearest neighbors and artificial neural network models, using modified mid-level data fusion, provide the best performance, misclassified only one sample. Machine learning in combination with modified mid-level data fusion strategy provides effective classification of Chenpi samples from different geographical origins. Pattern recognition (psychology) ; Artificial intelligence ; Mathematics ; Computer science Spectroscopy and Chemometric Analyses ; Traditional Chinese Medicine Analysis ; Identification and Quantification in Food
Authors: Yuta Tsuchiya | Yuhei Hirono | Rei Sonobe
Source: Ecological Informatics (article, 2025-08-18)
Field: Chemistry — Analytical Chemistry

Estimating chlorophyll content in tea leaves using spectral reflectance and deep learning methods Accurate estimation of chlorophyll content in tea leaves is essential for evaluating plant health, managing fertilization, and optimizing harvest timing in precision agriculture. This study investigates the use of hyperspectral reflectance data (400–850 nm, 5 nm intervals; 91 bands) to estimate chlorophyll content in tea leaves ( Camellia sinensis ) using three deep learning models: a one-dimensional convolutional neural network (1D–CNN) tailored for spectral regression, a vision transformer (ViT) adapted for one-dimensional inputs, and a self-supervised learning (SSL) model with regression. The key innovation of this study is the introduction of a self-supervised learning framework specifically adapted for spectral data, in which an autoencoder is first trained on unlabeled spectra to learn compact and noise-tolerant representations. These pretrained features are then used in a downstream regression task to predict chlorophyll content, allowing effective use of limited labeled data. To our knowledge, this is the first application of SSL in chlorophyll estimation using high–resolution leaf–level spectral measurements. Among the three models, the SSL approach achieved the highest accuracy, with a root mean square error (RMSE) of 3.33 μg/cm 2 , outperforming both the 1D–CNN (5.05 μg/cm 2 ) and ViT (4.28 μg/cm 2 ). These findings demonstrate that SSL is particularly effective for capturing subtle spectral patterns and improving prediction performance, especially when labeled data are scarce. This study highlights the potential of combining hyperspectral sensing with advanced representation learning to non–destructively monitor chlorophyll dynamics in tea cultivation, supporting more sustainable and data–driven agricultural practices. • SSL model outperformed 1D-CNN and ViT in chlorophyll estimation from reflectance. • SSL effectively used key chlorophyll absorption wavelengths. • Wavelengths in the 690–730 nm were confirmed as key for chlorophyll prediction. • SSL enables non-destructive, high-throughput chlorophyll monitoring in tea leaves. Reflectivity ; Chlorophyll ; Deep learning ; Content (measure theory) ; Remote sensing ; Computer science ; Artificial intelligence ; Botany ; Biology ; Mathematics ; Geology ; Optics ; Physics Spectroscopy and Chemometric Analyses ; Remote Sensing in Agriculture ; Leaf Properties and Growth Measurement
Authors: Lokeshvar Ravikumar | RAMAIYAN VELMURUGAN | Nithin Vidiyala | Pavani Sunkishala | VINOD KUMAR TERIVEEDHI
Source: Asian Journal of Pharmaceutical and Clinical Research (article, 2025-02-07)
Field: Materials Science — Biomaterials

NANOTECHNOLOGY-DRIVEN THERAPEUTICS FOR LIVER CANCER: CLINICAL APPLICATIONS AND PHARMACEUTICAL INSIGHTS Hepatocellular carcinoma (HCC) represents a significant threat to global health and is responsible for significant mortality rates worldwide. Conventional treatment options such as surgery and chemotherapy have inherent limitations. In order to remedy these deficits, the development of novel therapeutic strategies is essential. Nanomedicines have shown promise in HCC treatment as they offer improved stability, controlled release, and increased drug loading capacity. This review explores the application of nanoconstructs in HCC treatment, including active and passive targeting strategies. In addition, liver cell targeting approaches, targeting moieties, and conjugation chemistry for surface functionalization are investigated. A compact overview of various therapeutic approaches to HCC treatment is also given. Nanotechnology ; Liver cancer ; Cancer ; Materials science ; Medicine ; Internal medicine Nanoparticle-Based Drug Delivery ; Nanoplatforms for cancer theranostics ; Graphene and Nanomaterials Applications
Authors: Antonios Mouzakis | Periklis Panagopoulos | Dimitrios Papazoglou | Vasileios Petrakis
Source: Pathogens (review, 2025-10-26)
Field: Materials Science — Materials Chemistry

A Comprehensive Review of Nanoparticles in the Fight Against Antimicrobial Resistance (1) Background: The escalating crisis of multidrug-resistant (MDR) bacteria presents a formidable threat to global public health, necessitating the urgent development of alternative antimicrobial strategies. Nanoparticles (NPs) have emerged as a promising frontier in this effort, leveraging their unique physicochemical properties and multi-modal mechanisms of action to combat bacterial infections. This systematic review aims to comprehensively evaluate the current body of evidence on the dynamic interplay between nanoparticles and bacterial resistance. (2) Methods: A comprehensive search of electronic databases, including PubMed, Scopus, and Web of Science, was performed using a combination of keywords and Medical Subject Headings (MeSH) terms to identify relevant primary research articles. Eligibility criteria focused on studies evaluating the antimicrobial effects of nanoparticles on MDR bacterial strains, reporting on mechanisms of action, efficacy, or resistance development. (3) Results: The synthesis of findings revealed that nanoparticles exert their antimicrobial effects through multiple pathways, including the generation of reactive oxygen species (ROS), direct disruption of bacterial membranes, and the release of toxic ions. However, the analysis also confirmed that bacteria have evolved sophisticated defense mechanisms against nanoparticles, including surface modifications that prevent adhesion, upregulation of efflux pumps, and chemical neutralization of toxic ions. (4) Conclusions: Nanoparticles represent a potent and versatile tool in the global effort to combat antimicrobial resistance. Their long-term efficacy is not guaranteed, as bacteria have shown a remarkable capacity for adaptation. The future of this field lies in the development of rationally designed nanoparticle systems that not only possess intrinsic antimicrobial activity but also actively disarm bacterial resistance mechanisms. This includes the strategic use of synergistic combinations with conventional antibiotics and the exploration of resistance-agnostic approaches like nanotoxoid vaccines. Antimicrobial ; Antibiotic resistance ; Bacteria ; Efflux ; Nanoparticle ; Drug resistance Nanoparticles: synthesis and applications ; Antimicrobial Peptides and Activities ; Antimicrobial agents and applications
Authors: Amir Nasrolahi Shirazi | Rajesh Vadlapatla | Ajoy Koomer | Anthony Nguyen | Vian Khoury | Keykavous Parang
Source: Pharmaceutics (review, 2025-08-28)
Field: Materials Science — Biomaterials

Peptide-Based Inorganic Nanoparticles as Efficient Intracellular Delivery Systems <b>Background/Objectives</b>: Peptide-based inorganic nanoparticles (PINPs) have emerged as promising candidates for intracellular delivery due to their unique structural and functional attributes. These hybrid nanostructures combine the high surface area and tunable optical/magnetic properties of metal cores (e.g., Au, Ag, Fe<sub>3</sub>O<sub>4</sub>) with the biocompatibility, targeting specificity, and responsive behavior of peptides. In particular, peptides with amphipathic or cell-penetrating features could facilitate efficient transport of molecular cargos across cellular membranes while enabling stimulus-responsive drug release in target tissues. <b>Methods</b>: We review key synthesis methods (especially green, peptide-mediated one-pot approaches), functionalization strategies (e.g., thiol-gold bonds, click chemistries), and characterization techniques (TEM, DLS, FTIR, etc.) that underpin PINP design. In addition, we highlight diverse peptide classes (linear, cyclic, amphipathic, self-assembling) and their roles (targeting ligands, capping/stabilizing agents, reducing agents) in constructing multifunctional nanocarriers. <b>Results</b>: The prospects of PINPs are considerable: they enable targeted drug delivery with imaging/theranostic capability, improve drug stability and cellular uptake, and harness peptide programmability for precision nanomedicine. However, challenges such as in vivo stability, immunogenicity, and standardization of evaluation must be addressed. <b>Conclusions</b>: Overall, PINPs represent multifunctional platforms that could significantly advance drug delivery and diagnostic applications in the future. Nanomedicine ; Drug delivery ; Nanocarriers ; Biocompatibility ; Nanotechnology ; Peptide ; Surface modification ; Nanoparticle ; Chemistry ; Combinatorial chemistry ; Materials science ; Biophysics ; Biochemistry ; Biology Nanoparticle-Based Drug Delivery ; Advanced Nanomaterials in Catalysis ; Graphene and Nanomaterials Applications
Authors: Julia Nowak‐Jary | Beata Machnicka
Source: International Journal of Molecular Sciences (review, 2025-09-03)
Field: Materials Science — Materials Chemistry

Toxicity of Magnetic Nanoparticles in Medicine: Contributing Factors and Modern Assessment Methods With the rapid evolution of nanotechnology, magnetic iron oxide nanoparticles (MNPs)-primarily Fe<sub>3</sub>O<sub>4</sub> and γ-Fe<sub>2</sub>O<sub>3</sub>-have gained prominence in biomedicine. Their extensive specific surface area, tunable surface functionalities, and intrinsic magnetic characteristics render them highly versatile for diverse clinical applications, including tumor visualization through Magnetic Resonance Imaging (MRI), radiolabeling, targeted radiotherapy, hyperthermia, gene transfer, drug delivery, Magnetic Particle Imaging (MPI), magnetic blood filtration and theranostic strategies. Nevertheless, ensuring the biocompatibility and non-toxicity of these nanostructures remains a fundamental prerequisite for their medical implementation. Hence, it is essential to continuously refine our understanding of MNP-related toxicity and pursue comprehensive research on this front. This article consolidates up-to-date insights into the evaluation of MNPs' toxicological profiles, emphasizing the influence of physicochemical properties such as morphology, surface modifications, and electrostatic characteristics, along with operational factors like dosage and administration routes. Traditional toxicity testing strategies, including in vitro assays as first-line screening tools, together with standard ex vivo and in vivo models, are discussed. Special attention is given to the emerging role of New Approach Methodologies (NAMs), such as organoid formation, 3D bioprinting, in ovo chicken embryo assays, and image cytometry. These techniques offer ethical, human-relevant, and informative alternatives to animal testing, supporting more predictive and translationally relevant toxicity assessment of MNPs. Taken together, the integration of conventional assays with innovative NAMs, alongside careful consideration of physicochemical and operational factors, is essential to translate the laboratory promise of MNPs into safe and clinically effective nanomedicines. Toxicity ; Magnetic nanoparticles ; Risk assessment ; Nanotechnology ; Nanoparticle ; Medicine ; Pharmacology ; Computer science ; Internal medicine ; Materials science ; Computer security Nanoparticles: synthesis and applications ; Nanoparticle-Based Drug Delivery ; 3D Printing in Biomedical Research
Authors: Tenpattinam Shanmugam Saraswathi | M. Mothilal | Sarad Pawar Naik Bukke | Chandrashekar Thalluri | Ananda Kumar Chettupalli
Source: BioMedical Engineering OnLine (article, 2025-11-21)
Field: Materials Science — Biomaterials

Recent advances in potential drug nanocarriers for CNS disorders: a review The combination of traditional pharmacology and nanotechnology provides valuable opportunities for drug delivery to the CNS. Gains in the design of nanocarrier systems have great potential for addressing the limits of BBBs and for improving therapeutic outcomes in neurological disease, but more research is necessary for the development of translational clinical studies. Nanocarriers ; Drug ; Drug delivery ; Targeted drug delivery ; Drug development ; Translational research Nanoparticle-Based Drug Delivery ; Advanced Drug Delivery Systems ; Graphene and Nanomaterials Applications
Authors: Yuanyuan Hu | Ruiqi Fang | Yibo Liu | Mengdi Fu | Shu‐Han Wang | Jia‐Wang Yuan | Qi Yang | Qiuchen Peng | Zhao‐Yang Wang | Xiuqin Li | Kai Li | Shuang‐Quan Zang
Source: Aggregate (article, 2025-08-20)
Field: Materials Science — Materials Chemistry

Temperature‐Inert Terbium Cluster Scintillator ABSTRACT X‐ray scintillators play a critical role in medical diagnostics and industrial applications by converting ionizing radiation into low‐energy photons. Among various scintillators, metal clusters are promising due to advantages such as atom‐precise structures, high heavy‐atom density, strong luminescence intensity, and low usage cost. Those exhibiting temperature‐inert radioluminescence properties show broad application potential in extreme environments and have attracted considerable attention. In this work, a comprehensive strategy incorporating triplet exciton recycling and fluoride‐bridge‐induced carrier traps was introduced in the design of a temperature‐inert cluster scintillator ( Tb 16 ). The introduction of rare earth elements facilitated high‐efficiency triplet exciton recycling during the radioluminescence process, endowing Tb 16 with a high light yield of 41380 ± 950 photons MeV −1 . Meanwhile, the presence of fluoride‐bridges in Tb 16 induced abundant carrier traps, and the charge carriers captured by these traps could be thermally released back to the excited state at high temperatures, effectively compensating for emission loss. As a result, the radioluminescence intensity of Tb 16 remains nearly unchanged over a temperature range from 300 to 540 K, demonstrating its strong application potential in variable‐temperature X‐ray imaging. Terbium ; Cluster (spacecraft) ; Scintillator ; Inert ; Materials science ; Chemistry ; Physics ; Optoelectronics ; Computer science ; Optics ; Luminescence ; Organic chemistry ; Operating system Lanthanide and Transition Metal Complexes ; Atomic and Subatomic Physics Research ; Luminescence Properties of Advanced Materials
Authors: Dorota Bartusik‐Aebisher | Kacper Rogóż | David Aebisher
Source: Pharmaceutics (review, 2025-05-23)
Field: Materials Science — Biomaterials

Nanoparticles for Glioblastoma Treatment GBM is the most common and aggressive primary brain tumor in adults, characterized by low survival rates, high recurrence, and resistance to conventional therapies. Traditional diagnostic and therapeutic methods remain limited due to the difficulty in permeating the blood-brain barrier (BBB), diffuse tumor cell infiltration, and tumor heterogeneity. In recent years, nano-based technologies have emerged as innovative approaches for the detection and treatment of GBM. A wide variety of nanocarriers, including dendrimers, liposomes, metallic nanoparticles, carbon nanotubes, carbon dots, extracellular vesicles, and many more demonstrate the ability to cross the BBB, precisely deliver therapeutic agents, and enhance the effects of radiotherapy and immunotherapy. Surface functionalization, peptide modification, and cell membrane coating improve the targeting capabilities of nanostructures toward GBM cells and enable the exploitation of their photothermal, magnetic, and optical properties. Furthermore, the development of miRNA nanosponge systems offers the simultaneous inhibition of multiple tumor growth mechanisms and the modulation of the immunosuppressive tumor microenvironment. This article presents current advancements in nanotechnology for GBM, with a particular focus on the characteristics and advantages of specific groups of nanoparticles, including their role in radiosensitization. Glioblastoma ; Nanoparticle ; Chemistry ; Cancer research ; Nanotechnology ; Medicine ; Materials science Nanoparticle-Based Drug Delivery ; Nanoplatforms for cancer theranostics ; Graphene and Nanomaterials Applications
Authors: Mohammed Alqarni | Ali Alqarni
Source: Scientific Reports (article, 2025-01-17)
Field: Materials Science — Materials Chemistry

Machine learning analysis of rivaroxaban solubility in mixed solvents for application in pharmaceutical crystallization This study investigates the use of machine learning models to predict solubility of rivaroxaban in binary solvents based on temperature (T), mass fraction (w), and solvent type. Using a dataset with over 250 data points and including solvents encoded with one-hot encoding, four models were compared: Gradient Boosting (GB), Light Gradient Boosting (LGB), Extra Trees (ET), and Random Forest (RF). The Jellyfish Optimizer (JO) algorithm was applied to tune hyperparameters, enhancing model performance. The LGB model achieved the best results, with an R<sup>2</sup> of 0.988 on the test set and low error rates (RMSE of 9.1284E-05 and MAE of 5.85322E-05), surpassing other models in predictive accuracy and generalizability. Parity plots confirmed the LGB model's close alignment between predicted and actual solubility values, highlighting its robust performance. Furthermore, 3D surface plots and partial effect plots demonstrated LGB's capacity to model solubility across different solvent systems, capturing complex interactions between T, w, and solvent effects. Finally, the LGB model predicted maximum solubility at a temperature of 305.76 K and a mass fraction of 0.753 in a dichloromethane + methanol mixture, providing valuable insights for solubility optimization in solvent selection. This work underscores the effectiveness of the LGB model for solubility prediction, with potential applications in formulation and experimental planning. Solubility ; Gradient boosting ; Crystallization ; Solvent ; Mass fraction ; Boosting (machine learning) ; Computer science ; Random forest ; Algorithm ; Machine learning ; Materials science ; Artificial intelligence ; Chemistry ; Biological system ; Organic chemistry Crystallization and Solubility Studies ; Computational Drug Discovery Methods ; Drug Solubulity and Delivery Systems