jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Sajid Imran | Yuan Gao | Wenhao Wang | Jun Zhu
Source: Organic & Biomolecular Chemistry (article, 2025-01-01)
Field: Chemistry — Organic Chemistry

Charge-driven stability and aromaticity of C<sub>2</sub>N<sub>2</sub>B<sub>2</sub>H<sub>4</sub> isomers: insights from a combined DFT and machine learning study Recent research has sparked significant interest in exploring the effects of BN unit doping on the electronic structure of isoelectronic and isostructural benzene analogs, driven by their promising applications in pharmaceuticals and material sciences. In this study, we provide the first comprehensive investigation of BN/CC isosterism in 2,5-dihydro-1,4,2,5-diazadiborinine and its isomers (1-13) through density functional theory (DFT) calculations and machine learning analysis. Our findings reveal that isomers featuring jointed N-B-N-B linkages (1-4) exhibit the highest thermodynamic stability, attributed to alternatively distributed charge across the ring, facilitating enhanced electron delocalization. In contrast, isomers with disjointed B and N atoms, or those containing B-B and N-N bonds, exhibit greater charge separation, leading to reduced stability (5-12). Notably, 13 is the least stable due to its non-planarity and disrupted conjugation. Natural Resonance Theory (NRT) analysis confirms that the most stable isomers from three distinct categories (1, 5, and 11) form benzyne-like structures, characterized by triple bonding between N and B. Aromaticity evaluation using NICS(1)<sub><i>zz</i></sub>, HOMA, EDDB<sub>π</sub>, and MCI indexes confirms that 1-12 exhibit moderate to high aromaticity in the singlet state (S<sub>0</sub>), whereas 13 is nonaromatic. To evaluate the relative contributions of different aromaticity descriptors, Principal Component Analysis (PCA) was performed, revealing that NICS(1)<sub><i>zz</i></sub> is the most reliable predictor of aromaticity. Additionally, iterative linear regression modeling and PCA were used to determine the primary factors governing thermodynamic stability. Our results conclusively identify NPA charge distribution as the most significant predictor of stability, while bond order and aromaticity are less important. These insights provide a quantitative framework for understanding BN-heterocycle stability, offering valuable design strategies for tailoring the electronic properties of novel heterocyclic compounds for targeted applications in materials science and drug discovery. Aromaticity ; Charge (physics) ; Stability (learning theory) ; Computational chemistry ; Chemistry ; Physics ; Computer science ; Machine learning ; Molecule ; Organic chemistry ; Quantum mechanics Organoboron and organosilicon chemistry ; Crystallography and molecular interactions ; Synthesis and Properties of Aromatic Compounds
Authors: Zenan Shi | Lijun Liao | Shujun Peng | Yanying Wei | Libo Li
Source: Green Energy & Environment (article, 2025-09-17)
Field: Materials Science — Materials Chemistry

Computational screening of covalent organic frameworks for He purification with adsorption or membrane separation Separating He from CH 4 or N 2 is crucial for natural gas He extraction, a prevailing industrial approach. Herein, molecular simulation and machine learning (ML) were combined to screen 801 experimentally synthesized COFs for He/CH 4 and He/N 2 separation, either by means of adsorption or membrane separation. Top 10 COFs for 4 different gas separation purposes (CH 4 /He or N 2 /He separation with either adsorption or membrane) were identified respectively. The highest adsorption performance score (APS mix , defined as the product of working capacity and adsorption selectivity for mixture gas) reached 447.88 mol/kg and 49.45 mol/kg for CH 4 /He and N 2 /He, with corresponding adsorption selectivity of 115.56 and 30.33. He permeabilities of 1.5 × 10 6 or 1.2 × 10 6 Barrer were achieved for equimolar He/CH 4 or He/N 2 mixture gas separations, accompanied by permselectivity of 5.47 and 11.80 well surpassing 2008 Robeson’s upper bound. Best performing COFs for adsorption separation are 3D COFs with pore diameter below 0.8 nm while those for membrane separation are 2D COFs with large pores. Additionally, ML models were developed to predict separation performance, with key descriptors identified. The mechanism for how COFs’ structure affects their separation performance was also revealed. • Top 10 COFs for 4 different gas separation purposes are identified respectively. • He/CH 4 or He/N 2 mixture gas separations well surpass Robeson’s upper bound. • 3D COFs (PLD < 0.8 nm) favor adsorption, 2D COFs favor membrane separation. • ML models predict separation performance and identify key structural descriptors. • The mechanism of how COF structure influences separation performance is revealed. Adsorption ; Selectivity ; Gas separation ; Membrane ; Covalent bond ; Selective adsorption ; Membrane technology Covalent Organic Framework Applications ; Metal-Organic Frameworks: Synthesis and Applications ; Membrane Separation and Gas Transport
Authors: Yunpeng Wang | Chengming Li | Yang Yang | Chaochao Ma | Xiaojiao Zhao | Jiacheng Li | Lin Wei | Yang Li
Source: ACS Applied Materials & Interfaces (article, 2025-03-03)
Field: Materials Science — Electronic, Optical and Magnetic Materials

A Surface-Enhanced Raman Spectroscopy Platform Integrating Dual Signal Enhancement and Machine Learning for Rapid Detection of Veterinary Drug Residues in Meat Products The detection and quantification of veterinary drug residues in meat remain a significant challenge due to the complex background interference inherent to the meat matrix, which compromises the stability and accuracy of spectroscopic analysis. This study introduces an advanced label-free surface-enhanced Raman spectroscopy (SERS) platform for the precise identification and quantification of veterinary drugs. By employing a dual enhancement strategy involving sodium borohydride activation and calcium ion-deuterium oxide guidance, this platform achieves the efficient capture and signal amplification of drug molecules on highly active nanoparticles. High-quality SERS spectra were obtained for carprofen, doxycycline hydrochloride, chloramphenicol, and penicillin G sodium salt, enabling accurate classification and interference suppression. In addition, the application of machine learning algorithms, including PCA-LDA, heatmap, and decision tree modeling, allows for accurate differentiation of mixed drug samples. Quantitative analyses in meat samples were achieved through Raman intensity ratios and multivariate curve resolution-alternate least-squares (MCR-ALS) analysis, with results showing high consistency with high-performance liquid chromatography (HPLC) measurements. These findings highlight the potential of this SERS-based platform for accurate and rapid detection of multicomponent veterinary drug residues in complex food matrices. Materials science ; Surface-enhanced Raman spectroscopy ; Raman spectroscopy ; Veterinary drug ; Chromatography ; Chemistry ; Raman scattering ; Optics Gold and Silver Nanoparticles Synthesis and Applications ; Spectroscopy Techniques in Biomedical and Chemical Research ; Biosensors and Analytical Detection
Authors: Dandan Song | Leyuan Cui | Ruixuan Qin | Gang Fu
Source: JACS Au (article, 2025-06-11)
Field: Materials Science — Materials Chemistry

Influence of Oxygen Vacancy Distribution on CO<sub>2</sub> Hydrogenation: A Case Study of ZnO and In<sub>2</sub>O<sub>3</sub> Oxygen vacancies (OVs) on metal oxide surfaces are widely recognized as catalytically active sites; however, the impact of their distribution on the catalytic performance remains underexplored. In this study, we used density functional theory (DFT) calculations combined with a machine learning potential to investigate the distribution of OVs on the ZnO-(10 <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mover><mml:mn>1</mml:mn> <mml:mo>-</mml:mo></mml:mover> </mml:math> 0) surface and their role in CO<sub>2</sub> hydrogenation. We efficiently analyzed over 700,000 potential OV configurations by reducing them to unique, irreducible structures using the self-developed DefectMaker program. Our results revealed that higher OV concentrations led to the formation of linear OV structures, which, despite their energetic stability, exhibited lower CO<sub>2</sub> hydrogenation efficiency compared to isolated OVs, due to the reduced surface polarization with linear OVs. Additionally, a comparative investigation on In<sub>2</sub>O<sub>3</sub> surfaces revealed a scattered distribution of OVs, maintaining the material's catalytic activity in CO<sub>2</sub> hydrogenation. This work provides a deeper understanding of defect engineering in metal oxides for a more efficient CO<sub>2</sub> conversion. Vacancy defect ; Oxygen ; Materials science ; Distribution (mathematics) ; Crystallography ; Chemistry ; Mathematics ; Mathematical analysis ; Organic chemistry Catalytic Processes in Materials Science ; Catalysts for Methane Reforming ; Catalysis and Oxidation Reactions
Authors: Md. Nazim Uddin Chy | Tanvir Ahmed | Ramani Kumar Sarkar | Md. Zohurul Islam | Md Rahmatuzzaman Rana
Source: Applied Food Research (article, 2025-05-29)
Field: Chemistry — Analytical Chemistry

Artificial Neural Network and response surface methodology modeling-based optimization approach for the ultrasonication-assisted extraction of phenolics from mustard (BARI Sharisa-18) seed meal • Mustard variety BARI Sharisha-18 used for efficient antioxidant extraction. • UAE optimized for extracting TPC, TFC, and DPPH radical scavenging activity. • Three optimization approaches (RSM-SA-DF, RSM-GA-DF, ANN-GA-DF) were used. • Model predictions closely matched experimental results, proving reliability. • UAE outperformed conventional extraction, with higher yields and efficiency. This study utilized a mustard variety known as BARI Sharisha-18 to efficiently extract antioxidants like TPC, TFC, and DPPH radical scavenging activity using ultrasound-assisted extraction (UAE) by optimizing key parameters, such as ultrasound temperature (U-temp), sonication time (S-time), solvent-to-material ratio (S/M), and ethanol concentration (EtOH). Response Surface Methodology (RSM) and an Artificial Neural Network (ANN) multilayer perceptron model were employed to improve reliability, with hyperparameter tuning focused on the number of neurons and activation functions to prevent overfitting. A Box-Behnken design was applied to train both models, varying the parameters: U-temp (40–70°C), S-time (20–60 minutes), EtOH (40–80%), and S/M (10–30 mL/g). Multi-objective optimization for TPC, TFC, and DPPH was managed using a desirability function (DF), leading to three optimization approaches: RSM-SA-DF, RSM-GA-DF, and ANN-GA-DF. RSM-SA-DF and RSM-GA-DF produced similar optimal conditions: S/M of 20.4 ml/g, EtOH concentration of 80%, U-temp of 40°C, and S-time of 60 minutes, yielding TFC of 225.2 mg, TPC of 1677 mg, and DPPH activity of 82%. ANN-GA-DF showed slight variations with EtOH at 67% and S/M at 15 mL/g, while maintaining U-temp at 40°C and S-time at 60 minutes, achieving TFC of 270.4 mg QE/100 g, TPC of 2092.4 mg GAE/100 g, and DPPH activity of 89%. In comparison, conventional solvent extraction (60°C for 120 min with 80% ethanol at 30 mL/g) yielded 1278.17 mg GAE/100g of TPC, 135.97 mg QE/100g of TFC, and 68.29% DPPH activity, significantly ( p > 0.05) lower than the yields achieved with UAE. Overall, this study demonstrated the strong predictive accuracy of these hybrid modeling-based optimization approaches and affirmed UAE as a robust, efficient, and sustainable alternative to CSE by yielding higher antioxidant content under milder conditions. Response surface methodology ; Sonication ; Extraction (chemistry) ; Artificial neural network ; Chromatography ; Biological system ; Computer science ; Chemistry ; Artificial intelligence ; Biology Spectroscopy and Chemometric Analyses ; Phytochemicals and Antioxidant Activities ; Meat and Animal Product Quality
Authors: David B. Olawade | Oluwaseun Fapohunda | Sunday Oluwadamilola Usman | Abiola Akintayo | Ayokunle Olalekan Ige | Yemi A. Adekunle | Adedapo O. Adeola
Source: Chemistry Africa (article, 2025-06-11)
Field: Materials Science — Materials Chemistry

Artificial Intelligence in Computational and Materials Chemistry: Prospects and Limitations Abstract Computational chemistry, at the intersection of theoretical chemistry and computer science, employs various models to analyze molecular structures and properties, enabling the understanding and prediction of intricate chemical processes. The integration of artificial intelligence (AI) has revolutionized several fields, particularly in materials chemistry, with applications spanning drug discovery, materials design, and quantum mechanics. However, challenges related to quantum system complexity, model interpretability, and data quality remain a few of the Achilles’ heel of AI applications. This paper provides an overview of AI’s evolution in computational and materials chemistry, focusing on several applications. AI’s transformative potential in materials chemistry is emphasized, facilitating precise material property predictions, crucial for industries reliant on materials innovation. In materials chemistry, AI has led to substantial advancements, enabling the rapid discovery of materials with tailored properties. Yet, the challenges of modeling complex quantum systems, achieving model interpretability, and accessing high-quality data remain. The integration of AI into computational and materials chemistry promises to reshape the field, revolutionizing chemical research, materials design, and technological innovation. In order to harness AI’s full potential, transparent AI models, advanced quantum simulations, optimized data utilization, scalable computing, interdisciplinary collaboration, and ethical AI practices are essential. Computer science ; Biochemical engineering ; Management science ; Chemistry ; Engineering Machine Learning in Materials Science ; Computational Drug Discovery Methods
Authors: Mohammed G. Kotp | Mohamed Gamal Mohamed | Shiao‐Wei Kuo
Source: Chemical Science (article, 2025-01-01)
Field: Materials Science — Polymers and Plastics

Conjugated microporous polymer electrodes for supercapacitors: recent progress, key challenges, and future directions Conjugated Microporous Polymers (CMPs) have shown great potential as a class of materials for supercapacitor electrodes, offering a distinctive blend of extensive surface area, tunable porosity, redox activity, and chemical stability. This review thoroughly explores the role of CMPs in advancing supercapacitor technology, focusing on their structural and chemical characteristics, energy storage mechanisms, and recent advancements in material design and device engineering. We highlight the synergistic integration of CMPs with carbon-based nanomaterials, metal oxides, and conductive polymers to create hybrid and composite systems that enhance conductivity and electrochemical performance. Recent studies demonstrate significant improvements in essential performance indicators, including mass-based capacitance, energy storage capacity, power output efficiency, and cycling stability, positioning CMPs as competitive alternatives to traditional carbon-based materials like activated carbon as well as graphene. Although progress has been made, issues with scalability, conductivity, and long-term stability are still major challenges, requiring further research and innovation. This review also explores future directions, emphasizing the potential of CMPs in flexible, wearable, and solid-state supercapacitors, as well as their integration into hybrid energy systems. By addressing current limitations and leveraging emerging trends, CMP-based supercapacitors hold immense promise for enabling the next generation of highly durable, sustainable, and efficient energy storage systems. This review seeks to inspire future research as well as collaboration in this sector, facilitating transformative advances in energy storage systems. Supercapacitor ; Conjugated microporous polymer ; Energy storage ; Electrochemical energy storage ; Microporous material ; Metal-organic framework ; Sustainable energy Conducting polymers and applications ; Supercapacitor Materials and Fabrication ; Electrochemical sensors and biosensors
Authors: Tingting Xia | Bingyuan Zeng | Jingwen Jiang | Tenghu Wu | Weiying Pang | Wenjing Wang | Jie Zhang | Kun Zhao
Source: Materials Chemistry Frontiers (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Photothermal effect promoting the reconstruction and mass-energy transfer for the enhancement of three-dimensional confinement catalysis Photothermal catalysis, as an emerging technology, has attracted much attention owing to its high efficiency and renewable sustainability. Photothermal therapy ; Materials science ; Photothermal effect ; Energy transfer ; Catalysis ; Mass transfer ; Nanotechnology ; Optoelectronics ; Photochemistry ; Chemical physics ; Chemistry ; Organic chemistry Machine Learning in Materials Science
Authors: Lei Zhang | W. D. Luo | Renxi Liu | Mohan Chen | Zhongbo Yan | Kun Cao
Source: Physical Review Materials (preprint, 2025-02-07)
Field: Materials Science — Materials Chemistry

Exploring the energy landscape of aluminas through machine learning interatomic potential Despite the widespread applications of alumina due to its rich polymorphism, the structures of many transitional aluminas remain unresolved. This work employs the neuroevolution potential (NEP) approach to accurately describe polymorphic aluminas. Its accuracy and generality are validated through molecular dynamics simulations under diverse thermodynamic and structural conditions. A structural search workflow has also been developed based on NEP, which, in conjunction with spectroscopic data and structural stability considerations, supports the energetic preference of the Smr\ifmmode \check{c}\else \v{c}\fi{}ok model over the Luo model for $\ensuremath{\gamma}$-Al${}_{2}$O${}_{3}$. This methodological framework provides a systematic approach for exploring polymorphic materials with intrinsic defects, such as Ga${}_{2}$O${}_{3}$. Interatomic potential ; Phase diagram ; Work (physics) ; Materials science ; Energy (signal processing) ; Workflow ; Diagram ; Computer science ; Phase (matter) ; Molecular dynamics ; Thermodynamics ; Physics ; Chemistry ; Computational chemistry Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; High-pressure geophysics and materials
Authors: Deepak Devadiga | Juchao Yan | Dheeraj Devadiga
Source: ACS Applied Energy Materials (review, 2025-02-06)
Field: Chemistry — Physical and Theoretical Chemistry

Recent Advances in Probing Electron Delocalization in Conjugated Molecules by Attached Infrared Reporter Groups for Energy Conversion and Storage This review article reports an overview of the recent developments in the field of electron delocalization study in organic conjugated molecules by utilizing the vibration frequencies exhibited by the attached functional groups such as nitrile (-C≡N), alkyne (-C≡C-), or carbonyl (-C=O). A brief introduction to electron delocalization, methods for study, and their importance is given first, followed by the application of infrared spectroscopy in organic molecules. Details of molecules with various infrared reporter groups have been explained in respective subsections based on the functional groups. All the reported organic molecules have been structured and presented with the electron delocalization properties studied using an infrared reporter group. Finally, an outlook on this recently promising, exciting, and interesting field of probing electron delocalization using infrared reporter groups is provided. Delocalized electron ; Conjugated system ; Electron delocalization ; Molecule ; Infrared ; Nitrile ; Infrared spectroscopy ; Chemistry ; Photochemistry ; Organic molecules ; Spectroscopy ; Electron ; Materials science ; Nanotechnology ; Physics ; Organic chemistry ; Polymer ; Optics Photochemistry and Electron Transfer Studies ; Organic Electronics and Photovoltaics ; Organic and Molecular Conductors Research
Authors: Sérgio A. V. Afonso | Hatice Mutlu
Source: European Journal of Lipid Science and Technology (article, 2025-04-21)
Field: Materials Science — Polymers and Plastics

The Fusion of Elemental Sulfur With Waste Cooking Oil‐Based Derivatives: Beyond Renewability ABSTRACT Elemental sulfur (S 8 ), previously the powerhouse of modern society, and waste cooking oil (WCO) are nowadays surplus industrial by‐products from oil‐refinery and food processing industries, respectively, generating growing environmental concerns and economic loss. These unavoidable wastes, which are inherent in the aforementioned processes, cannot be minimized without substantial advances in technology and/or significant modifications in consumer behavior. Indeed, there are ongoing research activities to enable a powerful strategy for upcycling these wastes into valuable and useful products, that is, conversion to high‐sulfur content polymeric materials (also known as polysulfides). Among those research activities, the ones focusing on the utilization of the inverse vulcanization (IV), the reverse version of the well‐known vulcanization process, provides a facile and straightforward method that allows the use of S 8 and the WCO derivatives as the respective olefin components essential for the process. Thus, in this mini‐review a brief overview of the recent outcomes, challenges, and opportunities of the valorization of WCO and S 8 toward the synthesis of high‐sulfur content polymeric materials are discussed. Besides, exemplary studies are provided to illustrate the versatility of these materials in applications ranging from energy storage to heavy metal remediation, as well as prospects for industrial use. Practical Applications : Beyond highlighting existing applications, this review serves as a comprehensive guide to exploring new opportunities in the field, paving the way for future research and development. Crucially, the discussion extends to industrial scalability, commercialization prospects, and closed‐loop production strategies, evaluating the feasibility of waste‐cooking derived IV polymers in next‐generation sustainable manufacturing and green technology solutions. Sulfur ; Fusion ; Chemistry ; Waste management ; Elemental analysis ; Environmental chemistry ; Environmental science ; Pulp and paper industry ; Organic chemistry ; Engineering Synthesis and properties of polymers ; Organic Chemistry Cycloaddition Reactions ; Epoxy Resin Curing Processes