jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Sujata Wankhede | Aniket Badule | Saloni Chaure | Anjali Damahe | Minakshi Damahe | Omji Porwal
Source: Journal of Advanced Scientific Research (article, 2025-06-30)
Field: Chemistry — Organic Chemistry

Challenges and Strategies in Prodrug Design: A Comprehensive Review The development of prodrugs represents an indispensable paradigm in contemporary pharmaceutical sciences, enabling the circumvention of intrinsic limitations associated with conventional therapeutics. By virtue of their capacity to enhance solubility, bioavailability, metabolic stability, and tissue specificity, prodrugs facilitate the optimization of pharmacokinetic and pharmacodynamics parameters, thereby augmenting therapeutic efficacy. This review meticulously delineates the fundamental principles governing prodrug design, elucidates the intrinsic challenges encountered in their development, and explores state-of-the-art strategies employed to ameliorate these limitations. A systematic evaluation of peer-reviewed literature was undertaken, encompassing authoritative sources indexed in PubMed, Scopus, and Web of Science. The selection criteria encompassed studies that expound upon chemical modification strategies, site-specific activation mechanisms, and computational methodologies for rational prodrug design. The synthesis of data was executed with rigorous scrutiny to ensure the exclusion of biases and the inclusion of seminal advancements in nanotechnology, enzyme-targeted activation, and predictive modelling. The findings underscore the pivotal role of linker chemistry, enzymatic selectivity, and stimuli-responsive constructs in refining prodrug efficacy. The advent of artificial intelligence-driven predictive models and precision medicine approaches has further revolutionized the domain, facilitating the bespoke tailoring of prodrug candidates to individual patient profiles. Despite these advancements, formidable challenges persist, particularly in ensuring precise activation kinetics, circumventing regulatory constraints, and achieving scalable industrial synthesis. The future trajectory of prodrug research necessitates a confluence of interdisciplinary expertise, integrating computational modelling, advanced bioconjugation techniques, and sustainable synthetic methodologies. A concerted effort towards the refinement of assisted delivery and biomarker-guided activation will indubitably propel the next generation of prodrugs towards clinical and commercial fruition, thereby redefining the landscape of targeted therapeutic intervention. Prodrug ; Drug discovery ; Biosimilar ; Biopharmaceutical ; Pharmaceutical industry ; Scrutiny Click Chemistry and Applications ; Biochemical and Structural Characterization ; HIV/AIDS drug development and treatment
Authors: Jiezhen Hu | Huan Huang | Xin Hu | Peichang Deng | Zheng Guo | Jia-Hong Chen | Zhiwen Tu
Source: Journal of Chemistry (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Nonuniform Corrosion in the Vertical Direction of Q345 Steel in a Real Ocean Environment The aim of this investigation was to study nonuniform corrosion in the vertical direction of Q345 steel in the South China Sea. First, a metal corrosion monitoring system was designed and applied based on the coulometer principle and array electrode technology. Then, through coulometer testing, weightlessness analysis, electrochemical testing, and surface morphology observation, the corrosion behavior of Q345 steel used in an offshore wind turbine in Zhanjiang Bay in the South China Sea was studied. The results revealed that within the first 30 days of the experiment, the array electrodes connected by electricity exhibited sacrificial anode/cathode protection with self‐coupling. Therefore, the corrosion rates of the electrodes were slowed. Between 60 and 120 days, the array electrodes underwent a transition in the anode/cathode region. In actual marine environments, the most severely corroded areas of long metal structures were near the sea surface and seabed and had to be prioritized for protection. Chemistry ; Corrosion Corrosion Behavior and Inhibition ; Concrete Corrosion and Durability ; Structural Integrity and Reliability Analysis
Authors: Shahriar Jahani | Ali Akbari | Yousef Kazemzadeh | Soroush Ahmadi | Azizollah Khormali
Source: International Journal of Chemical Engineering (article, 2025-01-01)
Field: Chemistry — Analytical Chemistry

Impact of Asphaltene on Water‐in‐Oil Emulsion Stability This study investigates the influence of asphaltene as a natural surfactant on the formation and stability of water‐in‐oil (W/O) emulsions under both ambient (27°C, 14.7 psi) and reservoir conditions (up to 80°C and 4500 psi). Emulsions were formulated using synthetic oil composed of toluene and paraffin (60:40), with variable water volumes (10%–50%), asphaltene concentrations (1000–50,000 ppm), and salt types (NaCl, Na 2 SO 4 , CaCl 2 , and MgCl 2 ) at six different concentrations (1000–50,000 ppm). The findings reveal that a water content of 30% yields the most stable emulsion, with the smallest average droplet area of 1.144 μm 2 observed in the presence of MgCl 2 at 10,000 ppm. Increasing asphaltene concentration from 1000 to 10,000 ppm reduced average droplet size from 3.45 to 1.19 μm 2 and increased phase separation resistance, as confirmed by centrifuge tests. Under reservoir conditions, increasing pressure from 1500 to 2500 psi enhanced emulsion stability, with the viscosity rising from 3.012 to 3.183 cP. However, at higher pressures (3500–4500 psi), viscosity decreased to 2.33 and 1.79 cP, respectively, indicating reduced stability due to asphaltene precipitation and reduced interfacial activity. Temperature rises also had a destabilizing effect: instability at 40°C increased 2.94 times, and at 80°C, it rose 4.66 times compared to ambient temperature, based on water droplet area and distribution. These insights emphasize the sensitivity of W/O emulsions to salinity, pressure, and temperature, offering valuable implications for the design of effective emulsion‐based enhanced oil recovery (EOR) processes. Asphaltene ; Emulsion ; Petroleum engineering ; Environmental science ; Materials science ; Chemical engineering ; Engineering Petroleum Processing and Analysis ; Enhanced Oil Recovery Techniques ; Hydrocarbon exploration and reservoir analysis
Authors: Muhammad Iftikhar | Anis Ali Shah
Source: Functional Plant Biology (article, 2025-10-05)
Field: Materials Science — Materials Chemistry

Green-synthesized ZnO and MgO nanoparticles modulate physiology and antioxidant defense in maize under alkaline stress Alkaline stress severely impairs the growth and yield of Zea mays L. by disrupting physiological and biochemical functions. This study evaluated green-synthesized ZnO and MgO nanoparticles (NPs), prepared using neem and licorice extracts, for mitigating alkaline stress. NPs were nanosized, crystalline, and functionalized by phytochemicals, confirmed by scanning electron microscopy, FT-IR spectroscopy, UV-vis spectroscopy, and energy dispersive X-ray spectroscopy. A pot experiment using NPs (25-200 ppm) under control and alkaline stress assessed morphological, physiological, biochemical, and ionic responses. Alkaline stress reduced root fresh and dry weight to 2.60 and 0.66 g (-59.6%, -31.0%), shoot fresh and dry weight to 2.60 and 0.38 g (-59.6%, -70.0%), and chlorophyll a, b, and carotenoids to 1.31, 0.67, and 2.40 mg g-1 (-62.4%, -54.7%, -62.8%), whereas it increased malondialdehyde (MDA) (244.6%), H₂O₂ (457.7%), and relative membrane permeability (RMP) (55.9%). The combined ZnO (50 ppm) and MgO (50 ppm) treatment improved chlorophyll a, b, and carotenoids to 3.48, 1.48, and 6.45 mg g-1 (165.4%, 120.3%, 168.5%), and total soluble protein (392.8%), total protein (301.0%), proline (105.5%), glutathione (35.6%), and ascorbic acid (44.2%). Antioxidant enzymes increased, with superoxide dismutase at 29.52 U mg-1 (452.8%), peroxidase at 24.44 U mg-1 (862%), and ascorbate peroxidase at 51.62 U mg-1 (560%), whereas MDA, H2O2, and RMP (-78.1%) were reduced. High NP concentrations (ZnO 100 ppm + MgO 100 ppm) were toxic. Moderate ZnO and MgO NP doses enhanced resilience, yield stability, and sustainable agriculture. Antioxidant ; Malondialdehyde ; Carotenoid ; Superoxide dismutase ; Ascorbic acid ; Proline ; Chlorophyll ; Oxidative stress ; Peroxidase Nanoparticles: synthesis and applications ; Plant Stress Responses and Tolerance
Authors: Ziyuan Liu | Tingsong Zhang | Haoyuan Ding | Zhongshi Wang | Hongzhen Wang | Lu Zhou | Yujia Dai | Yiqing Xu
Source: Agronomy (article, 2025-08-06)
Field: Chemistry — Analytical Chemistry

Quantitative Analysis of Adulteration in Anoectochilus roxburghii Powder Using Hyperspectral Imaging and Multi-Channel Convolutional Neural Network Adulteration detection in medicinal plant powders remains a critical challenge in quality control. In this study, we propose a hyperspectral imaging (HSI)-based method combined with deep learning models to quantitatively analyze adulteration levels in Anoectochilus roxburghii powder. After preprocessing the spectral data using raw, first-order, and second-order Savitzky–Golay derivatives, we systematically evaluated the performance of traditional machine learning models (Random Forest, Support Vector Regression, Partial Least Squares Regression) and deep learning architectures. While traditional models achieved reasonable accuracy (R2 up to 0.885), their performance was limited by feature extraction and generalization ability. A single-channel convolutional neural network (CNN) utilizing individual spectral representations improved performance marginally (maximum R2 = 0.882), but still failed to fully capture the multi-scale spectral features. To overcome this, we developed a multi-channel CNN that simultaneously integrates raw, SG-1, and SG-2 spectra, effectively leveraging complementary spectral information. This architecture achieved a significantly higher prediction accuracy (R2 = 0.964, MSE = 0.005), demonstrating superior robustness and generalization. The findings highlight the potential of multi-channel deep learning models in enhancing quantitative adulteration detection and ensuring the authenticity of herbal products. Hyperspectral imaging ; Convolutional neural network ; Channel (broadcasting) ; Chemistry ; Computer science ; Pattern recognition (psychology) ; Artificial intelligence ; Telecommunications Spectroscopy and Chemometric Analyses ; Essential Oils and Antimicrobial Activity ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Daniel Ungureanu | Gabriel Marc | Mihaela Duma | Radu Tamaian | Dan Cristian Vodnar | Brînduşa Tiperciuc | Cristina Moldovan | Ioana Ionuț | Anca Stana | Ovidiu Oniga
Source: Antibiotics (article, 2025-09-18)
Field: Chemistry — Organic Chemistry

Antimicrobial and Antibiofilm Activities of Some Antioxidant 3,4-Dihydroxyphenyl-Thiazole-Coumarin Hybrid Compounds: In Silico and In Vitro Evaluation <b>Background/Objectives</b>: In this study, we aimed to investigate the antimicrobial and antibiofilm activity of seven hydroxyphenyl-thiazolyl-coumarin hybrid compounds with antioxidant properties (<b>1a</b>-<b>g</b>), previously reported by our group. <b>Methods</b>: The compounds were evaluated in vitro through MIC, MBC, and MFC determinations, and percentage of biofilm (BF) inhibition and in silico, respectively, through molecular docking, molecular dynamics simulations, and ADMETox prediction. <b>Results</b>: All compounds showed antibacterial and antifungal activities. In terms of antibacterial activity, all the compounds were active on <i>Pseudomonas aeruginosa</i> (MICs = 15.62-31.25 μg/mL), <i>Enterococcus faecalis</i> (MICs = 15.62-31.25 μg/mL), and <i>Staphylococcus aureus</i> (MICs = 62.5-125 μg/mL). Regarding the antifungal activity, the effect against <i>Candida albicans</i> was similar to fluconazole (MIC = 15.62 μg/mL), compounds <b>1b</b> and <b>1g</b> being the most active against <i>Aspergillus brasiliensis</i> (MIC = 15.62 μg/mL). Furthermore, all compounds were both bactericidal and fungicidal. Regarding the antibiofilm activity, compounds <b>1d</b>-<b>g</b> showed superior <i>P. aeruginosa</i> BF inhibition compared to gentamicin. The in vitro results for the antibacterial activity were well correlated with the observations drawn in the molecular docking studies, where the best binding affinities (BAs) were observed against <i>P. aeruginosa</i> PAO1 GyrB subunit, and the molecular dynamics simulations confirmed the antibacterial mechanism of compounds <b>1a, 1b</b>, <b>1d</b>, <b>1f,</b> and <b>1g</b> through GyrB subunit inhibition. Regarding the antifungal activity, all compounds showed better BAs than fluconazole against CYP51 in all instances. ADMETox predictions concluded that all the compounds could have low gastrointestinal absorption and reduced risk of pharmacokinetic interactions. <b>Conclusions</b>: The investigated compounds bring novelty into the actual research due to their dual antibacterial and antibiofilm activity against biofilm-associated <i>P. aeruginosa</i> infections. Antimicrobial ; Fluconazole ; In silico ; Biofilm ; In vitro ; Antioxidant ; Antifungal Synthesis and biological activity ; Phenothiazines and Benzothiazines Synthesis and Activities ; Bioactive Compounds and Antitumor Agents
Authors: Dan Cui | Keke Pan | Huan Liu | Peipei Wang | Feng Yu
Source: Catalysts (article, 2025-09-01)
Field: Materials Science — Materials Chemistry

Effects of SiO2, Al2O3 and TiO2 Catalyst Carriers on CO-SCR Denitration Performance of Bimetallic CuCe Catalysts Nitrogen oxides (NOx) emissions pose environmental and health risks. Selective catalytic reduction (SCR) is effective for NOx removal, and using CO as a reductant can eliminate both NOx and CO. This study explores CuCe catalysts on SiO2, Al2O3, and TiO2 for CO-SCR. Results show catalytic activity relates to the synergy between lattice oxygen and CuCe species. TiO2 enhances this interaction, promoting Cu+ and lattice oxygen for NO adsorption and dissociation. The CuCe/TiO2 catalyst achieves 100% NO conversion at 300 °C and 40.2% at 100 °C, indicating excellent low-temperature performance. These findings are valuable for developing efficient SCR catalysts. Catalysis ; Bimetallic strip ; Chemistry ; Business ; Materials science ; Chemical engineering ; Organic chemistry ; Engineering Catalytic Processes in Materials Science ; Catalysts for Methane Reforming ; Catalysis and Oxidation Reactions
Authors: Meichen Liu
Source: Applied and Computational Engineering (article, 2025-06-10)
Field: Chemistry — Analytical Chemistry

The Application of Ultraviolet-Visible Spectrophotometry in Pharmaceutical Analysis Ultraviolet-visible (UV-Vis) spectrophotometry is an analytical technique based on the absorption characteristics of substances to light in the ultraviolet to visible spectrum. In the field of pharmaceutical analysis, this method is widely used due to its simplicity, high sensitivity, and low cost. This paper reviews the basic principles of UV-Vis technology and its applications in pharmaceutical analysis. By analyzing the absorption of light at specific wavelengths by drug molecules, this technique can provide important information about drug concentration, purity, and stability, which is crucial for ensuring the safety and efficacy of drugs. Spectrophotometry ; Ultraviolet ; Chemistry ; Ultraviolet visible spectroscopy ; Chromatography ; Materials science ; Optoelectronics ; Organic chemistry Analytical Methods in Pharmaceuticals
Authors: Ryo Sasaki | Kaiin Tou | Shoma Kamanoi | Junya Yoshida | Yoshihito Takabe | Yasuyuki Miura | Eiji Kamiya | Aki Hirayama | Tomohito Sekine
Source: Micromachines (article, 2025-05-15)
Field: Materials Science — Materials Chemistry

MXene-Based High-Performance Soft Pressure Sensor Using Gel–Deep Eutectic Solvent Composite MXene, a layered nanocarbon material, exhibits excellent conductivity and solubility. Its high sensitivity also makes it useful for soft pressure sensors. However, the compatibility between sensitivity and fast responses in resistance-change sensors remains a major issue. This study developed an MXene-based high-performance soft pressure sensor using a gel-deep eutectic solvent composite. The composite conductive material exhibited excellent solubility and printability in soft device fabrication. The aim of this work was to produce a high-quality soft pressure sensor that exhibited quick responses over a wide sensitivity range for detecting applied pressure. The sensors achieved high performance in terms of a high-speed response (40 ms) and good sensitivity (-0.0109 kPa<sup>-1</sup>). These results represent an advance in intelligent wearable sensing systems by combining materials science and electronic devices. Composite number ; Eutectic system ; Materials science ; Deep eutectic solvent ; Pressure sensor ; High pressure ; Composite material ; Soft sensor ; Mechanical engineering ; Process (computing) ; Computer science ; Alloy ; Engineering ; Engineering physics MXene and MAX Phase Materials ; Advanced Sensor and Energy Harvesting Materials ; Dielectric materials and actuators
Authors: Mioara Petrus | Cristina Popa | Ana‐Maria Bratu | Vasile Bercu | Leonard Constantin Gebac | Delia-Mihaela Mihai | Ana-Cornelia Butcaru | F. Stănică | Ruxandra Gogoț
Source: Molecules (article, 2025-06-01)
Field: Chemistry — Analytical Chemistry

A Synergistic Approach Using Photoacoustic Spectroscopy and AI-Based Image Analysis for Post-Harvest Quality Assessment of Conference Pears This study presents a non-invasive approach to monitoring post-harvest fruit quality by applying CO<sub>2</sub> laser photoacoustic spectroscopy (CO<sub>2</sub>LPAS) to study the respiration of "Conference" pears from local and commercially stored (supermarket) sources. Concentrations of ethylene (C<sub>2</sub>H<sub>4</sub>), ethanol (C<sub>2</sub>H<sub>6</sub>O), and ammonia (NH<sub>3</sub>) were continuously monitored under shelf-life conditions. Our results reveal that ethylene emission peaks earlier in supermarket pears, likely due to post-harvest treatments, while ethanol accumulates over time, indicating fermentation-related deterioration. Significantly, ammonia levels increased during the late stages of senescence, suggesting its potential role as a novel biomarker for fruit degradation. The application of CO<sub>2</sub>LPAS enabled highly sensitive, real-time detection of trace gases without damaging the fruit, offering a powerful alternative to traditional monitoring methods. Additionally, artificial intelligence (AI) models, particularly convolutional neural networks (CNNs), were explored to enhance data interpretation, enabling early detection of ripening and spoilage patterns through volatile compound profiling. This study advances our understanding of post-harvest physiological processes and proposes new strategies for improving storage and distribution practices for climacteric fruits. Ethylene ; Postharvest ; PEAR ; Food spoilage ; Climacteric ; Photoacoustic imaging in biomedicine ; Environmental science ; Chemistry ; Ripening ; Food science ; Horticulture ; Biology ; Biochemistry Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Postharvest Quality and Shelf Life Management
Authors: Chanhui Lee | Hyang Mi Ko | Youjian Song | YongJun Jeong | Rodrigo Hormázabal | Sehui Han | Kyung‐Hoon Bae | Sung-Bin Lim | Sungwoong Kim
Source: ArXiv.org (preprint, 2025-02-05)
Field: Materials Science — Materials Chemistry

Mol-LLM: Multimodal Generalist Molecular LLM with Improved Graph Utilization Recent advances in large language models (LLMs) have led to models that tackle diverse molecular tasks, such as chemical reaction prediction and molecular property prediction. Large-scale molecular instruction-tuning datasets have enabled sequence-only (e.g., SMILES or SELFIES) generalist molecular LLMs, and researchers are now exploring multimodal approaches that incorporate molecular structural information for further gains. However, a genuinely multimodal, generalist LLM that covers a broad spectrum of molecular tasks has yet to be fully investigated. We observe that naive next token prediction training ignores graph-structural information, limiting an LLM's ability to exploit molecular graphs. To address this, we propose (i) Molecular structure Preference Optimization (MolPO), which facilitates graph usage by optimizing preferences between pairs of correct and perturbed molecular structures, and (ii) an advanced graph encoder with a tailored pre-training strategy to improve the effect of graph utilization by MolPO. Building on these contributions, we introduce Mol-LLM, the first multimodal generalist model that (a) handles a broad spectrum of molecular tasks among molecular LLMs, (b) explicitly leverages molecular-structure information, and (c) takes advantage of extensive instruction tuning. Mol-LLM attains state-of-the-art or comparable results across the most comprehensive molecular-LLM benchmark-even on out-of-distribution datasets for reaction and property prediction, where it surpasses prior generalist molecular LLMs by a large margin. Generalist and specialist species ; Graph ; Computer science ; Chemistry ; Biology ; Theoretical computer science ; Ecology Machine Learning in Materials Science ; Surface Chemistry and Catalysis
Authors: Xiaowei Zhang | Kaijie Yang | Chong Wang | Xiaoyu Liu | Ting Cao | Di Xiao
Source: npj Quantum Materials (article, 2025-12-02)
Field: Materials Science — Materials Chemistry

Twist-angle transferable continuum model and second flat Chern band in twisted MoTe2 and WSe2 We develop a twist-angle transferable continuum model for twisted transition metal dichalcogenide (tTMD) homobilayers, using tMoTe2 and tWSe2 as examples. All model parameters are extracted from density functional theory (DFT) calculations at a single twist angle (3.89°) and monolayer data. Our model captures both lattice relaxation effects and the long-range behavior of piezoelectric and ferroelectric potentials. Leveraging lattice relaxations obtained via machine learning force fields (MLFFs), the model can be efficiently transferred to other twist angles without requiring additional DFT calculations. It accurately reproduces the DFT band dispersions and quantum geometries across a wide range of twist angles. Furthermore, our model reveals that a second flat Chern band arises near 2° when the interlayer potential difference becomes comparable to the interlayer tunneling. This continuum model provides a clear understanding and starting point for engineering novel electronic phases in moiré TMDs through twist angles and lattice relaxations. Twist ; Lattice (music) ; Lattice model (finance) ; Density functional theory ; Electronic band structure ; Quantum 2D Materials and Applications ; Topological Materials and Phenomena ; Graphene research and applications