jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Kehui Pang | Mingjie Wen | Xiaoya Chang | Yabei Xu | Qingzhao Chu | Dongping Chen
Source: Physical Chemistry Chemical Physics (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

The thermal decomposition mechanism of RDX/AP composites: <i>ab initio</i> neural network MD simulations A neural network potential (NNP) is developed to investigate the decomposition mechanism of RDX, AP, and their composites. Utilizing an <i>ab initio</i> dataset, the NNP is evaluated in terms of atomic energy and forces, demonstrating strong agreement with <i>ab initio</i> calculations. Numerical stability tests across a range of timesteps reveal excellent stability compared to the state-of-the-art ReaxFF models. Then the thermal decomposition of pure RDX, AP, and RDX/AP composites is performed using NNP to explore the coupling effect between RDX and AP. The results highlight a dual interaction between RDX and AP, <i>i.e.</i>, AP accelerates RDX decomposition, particularly at low temperatures, and RDX promotes AP decomposition. Analyzing RDX trajectories at the RDX/AP interface unveils a three-part decomposition mechanism involving N-N bond cleavage, H transfer with AP to form Cl-containing acid, and chain-breaking reactions generating small molecules such as N<sub>2</sub>, CO, and CO<sub>2</sub>. The presence of AP enhances H transfer reactions, contributing to its role in promoting RDX decomposition. This work studies the reaction kinetics of RDX/AP composites from the atomic point of view, and can be widely used in the establishment of reaction kinetics models of composite systems with energetic materials. Thermal decomposition ; Ab initio ; Decomposition ; Composite material ; Materials science ; Thermal ; Mechanism (biology) ; Computational chemistry ; Chemistry ; Thermodynamics ; Physics ; Organic chemistry ; Quantum mechanics Thermal and Kinetic Analysis ; Energetic Materials and Combustion ; Epoxy Resin Curing Processes
Authors: Pravin D. Patil | Aparna Karvekar | Sakshi Salokhe | Manishkumar S. Tiwari | Shamraja S. Nadar
Source: International Journal of Biological Macromolecules (article, 2024-05-19)
Field: Materials Science — Materials Chemistry

When nanozymes meet enzyme: Unlocking the dual-activity potential of integrated biocomposites Nanotechnology ; Biochemical engineering ; Nanomaterials ; Computer science ; Limiting ; Leverage (statistics) ; Biosensor ; Biocatalysis ; Chemistry ; Catalysis ; Materials science ; Engineering ; Artificial intelligence ; Biochemistry Advanced Nanomaterials in Catalysis ; Nanocluster Synthesis and Applications ; Electrochemical sensors and biosensors
Authors: Farbod Ebrahimi | Anjali Kumari | Kristen Dellinger
Source: Advanced Sensor Research (article, 2024-12-20)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Integration of Nanoengineering with Artificial Intelligence and Machine Learning in Surface‐Enhanced Raman Spectroscopy (SERS) for the Development of Advanced Biosensing Platforms Surface-enhanced Raman spectroscopy (SERS) has emerged as a powerful tool for biomedical diagnosis, combining heightened sensitivity with molecular precision. The integration of artificial intelligence (AI) and machine learning (ML) has further elevated its capabilities, refining data interpretation, pattern prediction and bolstering diagnostic accuracy. This review chronicles advancements in SERS diagnostics, emphasizing the collaboration between ML and innovative nanostructures, substrates, and nanoprobes for SERS enhancement. We highlight breakthroughs in SERS-based point-of-care techniques and the nuanced detection of key biomarkers, from nucleic acids to proteins and metabolites. The article also addresses prevailing challenges, such as the need for standardized SERS methodologies and optimized platforms. Moreover, we touch upon the potential of portable SERS systems for clinical deployment and current efforts and challenges in clinical trials. In essence, this review positions the fusion of Nanoengineering, AI, ML, and SERS as the frontier for next-generation biomedical diagnostics. Nanoengineering ; Nanotechnology ; Surface-enhanced Raman spectroscopy ; Artificial intelligence ; Computer science ; Systems engineering ; Biochemical engineering ; Materials science ; Engineering ; Raman spectroscopy ; Raman scattering ; Physics Gold and Silver Nanoparticles Synthesis and Applications ; Biosensors and Analytical Detection ; Advanced biosensing and bioanalysis techniques
Authors: Pengfei Hou | Yumiao Tian | Yu Xie | Quan Li | Gang Chen | Fei Du | Jianzhong Wu | Yanming Ma | Xing Meng
Source: Angewandte Chemie International Edition (article, 2024-08-20)
Field: Materials Science — Materials Chemistry

Proton‐Driven Dynamic Behavior of Nanoconfined Water in Hydrophilic MXene Sheets Liquid water under nanoscale confinement has attracted intensive attention due to its pivotal role in understanding various phenomena across many scientific fields. MXenes serve an ideal paradigm for investigating the dynamic behaviors of nanoconfined water in a hydrophilic environment. Combining deep neural networks and an active learning scheme, here we elucidate the proton-driven dynamics of water molecules confined between V<sub>2</sub>CT<sub>x</sub> sheets using molecular dynamics simulation. Firstly, we have found that the Eigen and Zundel cations can inhibit water-induced oxidation by adjusting the orientation of water molecules, thus proposing a general antioxidant strategy. Besides, we also identified a hexagonal ice phase with abnormal bonding rules at room temperature, rather than only at ultralow temperatures as other studies reported, and further captured the proton-induced water phase transition. This highlighted the importance of protons in the maintaining stable crystal phase and phase transition of water. Furthermore, we discussed the conversions of different water structures and water diffusivity with changing proton concentrations in detail. The results provide useful guidance in practical applications of MXenes including developing antioxidant strategies, identifying novel 2D water phases and optimizing energy storage and conversion. MXenes ; Chemical physics ; Proton ; Molecular dynamics ; Phase (matter) ; Phase transition ; Molecule ; Materials science ; Nanotechnology ; Proton transport ; Chemistry ; Computational chemistry ; Physics ; Thermodynamics ; Organic chemistry MXene and MAX Phase Materials ; Graphene research and applications ; Advanced Memory and Neural Computing
Authors: Jianyang Feng | Lihong Shi | Dan Chang | Chuan Dong | Shaomin Shuang
Source: Chemical Engineering Journal (article, 2024-05-01)
Field: Materials Science — Materials Chemistry

Sub-30-Seconds ultrafast intelligent detection of glutathione using machine learning-guided handheld sensing platform based on mercury ion-mediated ratiometric fluorescence carbon dots Fluorescence ; Mercury (programming language) ; Detection limit ; Mobile device ; Computer science ; Naked eye ; Materials science ; Chemistry ; Nanotechnology ; Optics ; Chromatography Carbon and Quantum Dots Applications ; Advanced biosensing and bioanalysis techniques ; Biosensors and Analytical Detection
Authors: Mohammad Khodabakhshi | Masoud Bijani
Source: Results in Engineering (article, 2024-05-14)
Field: Materials Science — Biomaterials

Predicting scale deposition in oil reservoirs using machine learning optimization algorithms Scale deposition, a form of formation damage, not only affects the reservoir but also damages the well and equipment. This phenomenon occurs due to changes in temperature, pressure, and the injection of incompatible salt water, leading to ionic reactions. This study investigated permeability reduction due to scale deposition and examined how parameters such as temperature, pressure drop, and ion concentration affect the prediction accuracy. The scale deposits investigated in this study include CaSO4, BaSO4, and SrSO4. This paper uses Python to employ different machine-learning algorithms to predict the results. Each machine learning model has certain hyper-parameters that need adjustment. Failure to do so will result in reduced accuracy and incomplete interpretation of input data. The accuracy of the support vector regression (SVR) algorithm was significantly affected by the variation of the epsilon parameter in the dataset used. Therefore, before hyperparameter optimization, SVR had the lowest accuracy at 0.575. After adjusting the hyper-parameters, our findings show that SVR had the highest increase in R-squared value, which was 0.900, and the most minor growth in KNN, which went from 0.995 to 0.996. Additionally, the highest accuracy value for K-Nearest Neighbor is 0.996. Furthermore, most errors were related to SVR and XGBoost algorithms, while the most negligible errors were for the Decision Tree and KNN algorithms. Hyperparameter ; Support vector machine ; Algorithm ; Artificial intelligence ; Machine learning ; Python (programming language) ; Decision tree ; Computer science ; Mathematics Calcium Carbonate Crystallization and Inhibition ; Hydrocarbon exploration and reservoir analysis ; Hydraulic Fracturing and Reservoir Analysis
Authors: Bakr Ahmed Taha | Inas A. Al-Tahar | Ali J. Addie | Ali Basim Mahdi | Adawiya J. Haider | Yousif Al Mashhadany | Vishal Chaudhary | Norhana Arsad
Source: Applied Materials Today (article, 2024-05-21)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Nanophotonic catheters: A lens into the body for biosensing and biomedical imaging Nanophotonics ; Lens (geology) ; Biosensor ; Nanotechnology ; Optics ; Materials science ; Physics Gold and Silver Nanoparticles Synthesis and Applications ; Plasmonic and Surface Plasmon Research ; Advanced biosensing and bioanalysis techniques
Authors: Gopi Periyappillai | S. Sathiyamurthy | S. Saravanakumar
Source: Fibers and Polymers (article, 2024-07-19)
Field: Materials Science — Polymers and Plastics

Optimized Machine Learning Prediction and RSM Optimization of Mechanical Properties in Boiled Eggshell Filler-Added Biocomposites Materials science ; Filler (materials) ; Composite material ; Eggshell ; Response surface methodology ; Machine learning ; Computer science Natural Fiber Reinforced Composites ; Mechanical Engineering and Vibrations Research ; Additive Manufacturing and 3D Printing Technologies
Authors: Fouzia Mashkoor | Sayed Mohammed Adnan | Mohd Shoeb | Changyoon Jeong
Source: ACS Sustainable Chemistry & Engineering (article, 2024-04-12)
Field: Materials Science — Materials Chemistry

Waste-to-Wealth Strategy: Ti<sub>3</sub>AlC<sub>2</sub>-Max-Supported WS<sub>2</sub>/Halloysite Nanocomposite for the Removal of Nickel Metal Ions from Wastewater with Machine Learning Simulation and Subsequent Application in Supercapacitors We proposed an innovative approach employing the “waste-to-wealth” concept, transforming expended adsorbent into electrode materials for energy storage with a focus on sustainability. Ti3AlC2 MAX phase-decorated WS2 and halloysite (Mx-WS2-Hal NCs) were synthesized hydrothermally for the efficient removal of nickel metal ions from wastewater. Subsequently, the spent adsorbent, enriched with Ni2+ (Mx-WS2-Hal@Ni-adsorbed), was repurposed for supercapacitor applications, thus minimizing the environmental impact. The batch adsorption results revealed that WS2-Hal and 1, 3, and 5% Mx functionalized WS2-Hal NCs show removal efficiencies of 79.82, 86.146, 95.59, and 93.66%, respectively, for Ni2+. Furthermore, machine learning models were employed to predict the removal efficiency of Ni2+ onto the Mx-WS2-Hal NCs. The column research showed that the removal efficiency of Ni2+ was higher at low inlet solution concentration, low flow rate, or high column depth. The column experimental data presented the better fitting of Thomas and Yoon-Nelson model for the Ni2+ adsorption onto the Mx-WS2-Hal NCs. After adsorption, the expended Ni adsorbed Mx-WS2-Hal adsorbent was employed for energy storage to minimize the risk of causing additional pollution. The fabricated Mx-WS2-Hal@Ni-adsorbed NCs based symmetric supercapacitor device displayed 59.47 W h/kg of energy density at 0.583 kW/kg of power density. Moreover, it maintained a capacitance retention of 90% even after undergoing 10,000 cycles, highlighting the long-term sustainability and reliability of the proposed approach. Adsorption ; Nickel ; Halloysite ; Wastewater ; Materials science ; Chemical engineering ; Nanocomposite ; Metal ions in aqueous solution ; Energy storage ; Metal ; Chemistry ; Environmental engineering ; Nanotechnology ; Metallurgy ; Composite material ; Environmental science ; Organic chemistry ; Thermodynamics ; Power (physics) MXene and MAX Phase Materials ; Supercapacitor Materials and Fabrication ; Graphene and Nanomaterials Applications
Authors: Yahui Che | Yuqi Ni | Yang Jiao | Fengcai Lei | Chundong Liu | Xiaofei Zhao | Zhen Li | Chao Zhang | Jing Yu
Source: ACS Photonics (article, 2024-07-06)
Field: Materials Science — Electronic, Optical and Magnetic Materials

A Strategy for Accurate SERS Gas Detection: Skillful Integration of Mass-Productive Wafer-Scale SERS Substrate and Machine Learning–Assisted Multifeature Profiling The highly sensitive and precise detection of gaseous molecules continues to pose a significant challenge in surface-enhanced Raman scattering (SERS) to this day. To address this issue, a novel strategy is proposed here, which skillfully integrates mass-productive wafer-scale particle-in-quasi cavity (PIQC) optical structure and machine learning (ML)–assisted multifeature profiling method. This PIQC optical structure not only demonstrates low limit of detection for gaseous molecules (H2S: 10–10 v/v) but also exhibits high SERS stability (lowest relative standard deviation <10%) and yield (261 chips or 29 sensor matrix for one wafer), enabling it to provide massive high-quality Raman data for ML model (>72000 spectra for one sensor matrix), which coincidentally aligns with the utmost necessity in ML analysis. Benefiting from this, the ML model can conveniently and comprehensively integrate all Raman characteristic peaks of multiplex reporter molecules. In contrast with the common analytical method reliant solely on a single or limited Raman characteristic peaks of a singular reporter molecule, this approach can encompass more vibration information on the targeted gaseous molecule, thereby significantly augmenting the detection precision. Finally, we take H2S detection as an example, comprehensively verifying both the feasibility and advancement of this SERS strategy under various real-life scenarios. Profiling (computer programming) ; Wafer ; Nanotechnology ; Materials science ; Substrate (aquarium) ; Computer science ; Optoelectronics ; Biology Gold and Silver Nanoparticles Synthesis and Applications ; Biosensors and Analytical Detection ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Nicole Kate Borja | Christine Joy E. Fabros | Bonifacio T. Doma
Source: Energies (article, 2024-02-16)
Field: Chemistry — Inorganic Chemistry

Prediction of Hydrogen Adsorption and Moduli of Metal–Organic Frameworks (MOFs) Using Machine Learning Strategies For hydrogen-powered vehicles, the efficiency cost brought about by the current industry choices of hydrogen storage methods greatly reduces the system’s overall efficiency. The physisorption of hydrogen fuel onto metal–organic frameworks (MOFs) is a promising alternative storage method due to their large surface areas and exceptional tunability. However, the massive selection of MOFs poses a challenge for the efficient screening of top-performing MOF structures that are capable of meeting target hydrogen uptakes. This study examined the performance of 13 machine learning (ML) models in the prediction of the gravimetric and volumetric hydrogen uptakes of real MOF structures for comparison with simulated and experimental results. Among the 13 models studied, 12 models gave an R2 greater than 0.95 in the prediction of both the gravimetric and the volumetric uptakes in MOFs. In addition, this study introduces a 4-20-1 ANN model that predicts the bulk, shear, and Young’s moduli for the MOFs. The machine learning models with high R2 can be used in choosing MOFs for hydrogen storage. Metal-organic framework ; Adsorption ; Moduli ; Hydrogen ; Metal ; Materials science ; Computer science ; Chemistry ; Metallurgy ; Organic chemistry ; Physics Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; Covalent Organic Framework Applications
Authors: Mariya Aleksandrova | Nikolay Kurtev | И. М. Пандиев
Source: Applied Sciences (article, 2024-03-14)
Field: Materials Science — Materials Chemistry

Effect of MXene Nanosheet Sticking on Supercapacitor Device Performance Supercapacitors have garnered significant interest in recent years due to their high power density, rapid charge/discharge rates, and long cycle life. MXenes, a family of two-dimensional (2D) transition metal carbides/nitrides, have emerged as promising electrode materials for supercapacitors. However, one major challenge associated with incorporating MXenes in supercapacitor structures is the occurrence of sticking, wherein individual MXene flakes agglomerate, leading to reduced electrode performance. This review paper discusses various causes of sticking and approaches to preventing it, offering insights into the design and development of high-performance MXene-based supercapacitors. The morphology and size of MXene flakes, flake surface chemistry, thickness, surface area/volume ratio, electrode processing techniques (including solvent selection, additives incorporation, and deposition technology), and environmental factors were shown to be the basic factors resulting in sticking of MXene sheets. Among the strategies to mitigate this challenge, surface functionalization and passivation, integration with polymer matrices or carbon nanomaterials, and electrode processing optimization were considered. Possible paths for optimization and future directions of study, such as novel MXene compositions, understanding of interfaces and electrode–electrolyte interactions, development of advanced electrode architectures, and integration of energy storage systems, were assumed. Nanosheet ; Materials science ; Supercapacitor ; Business ; Nanotechnology ; Chemistry ; Electrode ; Capacitance MXene and MAX Phase Materials ; Advanced Memory and Neural Computing ; Supercapacitor Materials and Fabrication
Authors: Sitender Singh | Devender Singh | Preeti Siwach | Isha Gupta | Pawan Kumar
Source: Applied Research (review, 2024-12-17)
Field: Materials Science — Materials Chemistry

Synthesis Strategies for Rare Earth Activated Inorganic Phosphors: A Mini Review ABSTRACT Rare‐earth (RE)‐activated inorganic nanophosphors have garnered significant attention due to their unique optical properties and versatile applications in fields ranging from lighting and displays to biomedical imaging. The mini review provides a concise overview of recent advances in the synthesis strategies employed for the fabrication of these nanomaterials. Various synthesis techniques, including solid‐state, sol‐gel, solution combustion, hydrothermal, co‐precipitation, and microwave methods have been discussed, highlighting their advantages and limitations in tailoring the properties of nanophosphors. Furthermore, the role of different rare earth dopants and host matrices in controlling the luminescent properties of nanophosphors has been explored. Future perspectives and emerging trends in preparation of RE‐activated inorganic nanophosphors has also been discussed, with an emphasis on addressing current challenges and exploring new avenues for the fabrication of adequate and functional nanomaterials. Nanotechnology ; Nanomaterials ; Rare earth ; Materials science ; Phosphor ; Dopant ; Fabrication ; Doping ; Optoelectronics Luminescence Properties of Advanced Materials ; Inorganic Chemistry and Materials ; Ammonia Synthesis and Nitrogen Reduction
Authors: Seeun Oh | Hyeong-Geun Kim | Incheol Jeong | Dongyeon Kim | Hyeongmin Yu | Kang Taek Lee
Source: Journal of the Korean Ceramic Society (article, 2024-01-25)
Field: Materials Science — Materials Chemistry

Recent progress in oxygen electrodes for protonic ceramic electrochemical cells Abstract Protonic ceramic electrochemical cells, a promising technology for energy conversion and storage, have garnered significant interest in recent years owing to their superior low-temperature (&lt; 600 °C) performance relative to solid oxide electrochemical cells. However, the sluggish kinetics of oxygen electrodes have impeded further advancements. Despite considerable research efforts, the development of practically applicable oxygen electrodes remains challenging. We herein review the recent research focusing on the fundamental understanding and development of oxygen electrode materials. Furthermore, we provide a range of material design strategies for enhancing the catalytic activity of oxygen electrodes along with a concise overview of potential derivative applications. Finally, the perspectives and potential directions for the development of oxygen electrodes for high-performance protonic ceramic electrochemical cells are presented. Materials science ; Electrochemistry ; Ceramic ; Electrode ; Oxygen ; Chemical engineering ; Metallurgy ; Physical chemistry ; Chemistry Advancements in Solid Oxide Fuel Cells ; Electronic and Structural Properties of Oxides ; Advanced battery technologies research
Authors: Tajudeen A. Oyehan | Saheed A. Ganiyu | Christian Pfrang | Marc Walker | Eugenia Valsami‐Jones
Source: Chemical Engineering Journal (article, 2024-05-13)
Field: Materials Science — Materials Chemistry

Low cost MXene synthesis for regenerative adsorption of benzene, toluene, ethylbenzene and xylene (BTEX) MXenes, a group of emerging 2D metal carbides with unique properties and expanding potential applications, have attracted researchers’ interests. However, high synthesis costs and safety concerns have imposed limitations on their research and development. Here, we report a low-cost and safer synthesis method for a titanium-MXene, which we then test for its efficacy as an adsorbent for organics contamination in water. For the synthesis, we examine how different carbon forms (graphite and activated charcoal (AC)) impact the MXene properties and application. Instead of expensive and high-risk pure titanium as a precursor, cheap and easy-to-handle titanium oxide was used, while achieving lower-temperature synthesis through the molten-salt shielded synthesis approach. The resulting 3D MAX phases were separately etched into 2D-MXenes and characterized extensively. The synthesised MXenes were tested as adsorbents in adsorption experiments under various conditions. The data obtained were fitted to isotherm and kinetic models to understand the adsorption mechanisms. Although the AC-based MXene exhibited a much larger BET surface area compared to the graphite-based MXene (24 m2/g vs 2 m2/g respectively), other characterization results indicated that both materials had remarkably similar morphological and functional properties. Additionally, both MXenes effectively removed at least 80 % of the initial contaminant concentration. However, AC-based MXene is cheaper to produce and demonstrated superior regenerative capabilities over three cycles of regeneration and reuse. Also, its reuse potential is less affected by oxidation during the regeneration process. This study concludes that MXenes can be synthesized at a lower cost and highlights the crucial role of the carbon form used in synthesis for the MXene subsequent application. Specifically, AC-based MXenes show great potential for clean water recovery applications and could be a good candidate for analytical chemistry purposes. Ethylbenzene ; BTEX ; Toluene ; Benzene ; p-Xylene ; Adsorption ; Chemistry ; Xylene ; Organic chemistry ; Chemical engineering ; Environmental chemistry ; Engineering MXene and MAX Phase Materials ; Advanced Photocatalysis Techniques ; 2D Materials and Applications