jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Jiahui Chen | Zhihui Li | Yanlei Kang | Zhong Li
Source: Crystals (article, 2024-03-28)
Field: Materials Science — Materials Chemistry

Cocrystal Prediction Based on Deep Forest Model—A Case Study of Febuxostat To aid cocrystal screening, a deep forest-based cocrystal prediction model was developed in this study using data from the Cambridge Structural Database (CSD). The positive samples in the experiment came from the CSD. The negative samples were partly from the failure records in other papers, and some were randomly generated according to specific rules, resulting in a total of 8576 pairs. Compared with the models of traditional machine learning methods and simple deep neural networks models, the deep forest model has better performance and faster training speed. The accuracy is about 95% on the test set. Febuxostat cocrystal screening was also tested to verify the validity of the model. Our model correctly predicted the formation of cocrystal. It shows that our model is practically useful in practice. Febuxostat ; Cocrystal ; Computer science ; Econometrics ; Artificial intelligence ; Mathematics ; Chemistry ; Medicine ; Internal medicine Crystallization and Solubility Studies ; Crystallography and molecular interactions ; Metabolomics and Mass Spectrometry Studies
Authors: Ana Casas | Diego Rodríguez-Llorente | Guillermo Rodríguez-Llorente | Juan Garcı́a | Marcos Larriba
Source: Journal of Water Process Engineering (article, 2024-04-30)
Field: Chemistry — Spectroscopy

Machine learning screening tools for the prediction of extraction yields of pharmaceutical compounds from wastewaters Pharmaceutical compounds have become an increasingly important source of pollutants in wastewaters being conventional treatments ineffective in removing them, so they are commonly discharged into the environment. Pharmaceuticals can be successfully removed using liquid-liquid extraction, and COSMO-RS can be used to predict interactions and identify the most promising solvents. However, COSMOtherm models cannot account for key process parameters, which reduces the accuracy of these computational models. Therefore, there is a need for alternative computational approaches to accurately predict the extraction yields of pharmaceuticals which can incorporate both processing and interaction variables. This work used machine learning to predict the extraction yield of eleven pharmaceuticals using eight solvents. Six regression models and two classification models were explored. The best performance was obtained with ANN regressor (test MAE: 4.510, test R2: 0.884) and RF classifier (test accuracy: 0.938, test recall: 0.974). The RF regression analysis and classification also showed key extraction yield features: solvent-to-feed ratio, n–octanol–water partition coefficient, hydrogen bond and Van der Waals contributions to excess enthalpy, and pH distance to nearest pKa. Machine learning showed as an excellent tool for screening and selecting the most promising solvents and process conditions to remove pharmaceuticals from wastewater. Extraction (chemistry) ; Machine learning ; COSMO-RS ; Artificial intelligence ; Octanol ; Partition coefficient ; Random forest ; Yield (engineering) ; van der Waals force ; Computer science ; Predictive modelling ; Chemistry ; Biochemical engineering ; Chromatography ; Materials science ; Engineering ; Ionic liquid ; Molecule ; Organic chemistry Analytical Chemistry and Chromatography ; Water Quality Monitoring and Analysis ; Analytical chemistry methods development
Authors: Ana I. Furtado | Vasco D. B. Bonifácio | Raquel Viveiros | Teresa Casimiro
Source: Molecules (article, 2024-02-20)
Field: Chemistry — Analytical Chemistry

Design of Molecularly Imprinted Polymers Using Supercritical Carbon Dioxide Technology The design and development of affinity polymeric materials through the use of green technology, such as supercritical carbon dioxide (scCO<sub>2</sub>), is a rapidly evolving field of research with vast applications across diverse areas, including analytical chemistry, pharmaceuticals, biomedicine, energy, food, and environmental remediation. These affinity polymeric materials are specifically engineered to interact with target molecules, demonstrating high affinity and selectivity. The unique properties of scCO<sub>2</sub>, which present both liquid- and gas-like properties and an accessible critical point, offer an environmentally-friendly and highly efficient technology for the synthesis and processing of polymers. The design and the synthesis of affinity polymeric materials in scCO<sub>2</sub> involve several strategies. Commonly, the incorporation of functional groups or ligands into the polymer matrix allows for selective interactions with target compounds. The choice of monomer type, ligands, and synthesis conditions are key parameters of material performance in terms of both affinity and selectivity. In addition, molecular imprinting allied with co-polymerization and surface modification are commonly used in these strategies, enhancing the materials' performance and versatility. This review aims to provide an overview of the key strategies and recent advancements in the design of affinity polymeric materials using scCO<sub>2</sub>. Molecularly imprinted polymer ; Nanotechnology ; Polymer ; Supercritical fluid ; Molecular imprinting ; Supercritical carbon dioxide ; Materials science ; Environmentally friendly ; Selectivity ; Chemistry ; Organic chemistry ; Catalysis Analytical chemistry methods development ; Analytical Chemistry and Chromatography ; Polymer Surface Interaction Studies
Authors: karim mansouri | Fatemeh Bahmanzadegan | Ahad Ghaemi
Source: Scientific Reports (article, 2024-07-06)
Field: Materials Science — Materials Chemistry

Evaluation of hydrogen production via steam reforming and partial oxidation of dimethyl ether using response surface methodology and artificial neural network This study aims to develop two models for thermodynamic data on hydrogen generation from the combined processes of dimethyl ether steam reforming and partial oxidation, applying artificial neural networks (ANN) and response surface methodology (RSM). Three factors are recognized as important determinants for the hydrogen and carbon monoxide mole fractions. The RSM used the quadratic model to formulate two correlations for the outcomes. The ANN modeling used two algorithms, namely multilayer perceptron (MLP) and radial basis function (RBF). The optimum configuration for the MLP, employing the Levenberg-Marquardt (trainlm) algorithm, consisted of three hidden layers with 15, 10, and 5 neurons, respectively. The ideal RBF configuration contained a total of 80 neurons. The optimum configuration of ANN achieved the best mean squared error (MSE) performance of 3.95e-05 for the hydrogen mole fraction and 4.88e-05 for the carbon monoxide mole fraction after nine epochs. Each of the ANN and RSM models produced accurate predictions of the actual data. The prediction performance of the ANN model was 0.9994, which is higher than the RSM model's 0.9771. The optimal condition was obtained at O/C of 0.4, S/C of 2.5, and temperature of 250 °C to achieve the highest H<sub>2</sub> production with the lowest CO emission. Mole fraction ; Artificial neural network ; Dimethyl ether ; Response surface methodology ; Multilayer perceptron ; Mean squared error ; Hydrogen production ; Biological system ; Hydrogen ; Radial basis function ; Carbon monoxide ; Materials science ; Computer science ; Mathematics ; Chemistry ; Artificial intelligence ; Statistics ; Physical chemistry ; Organic chemistry ; Catalysis Catalytic Processes in Materials Science ; Catalysts for Methane Reforming ; Catalysis and Oxidation Reactions
Authors: Ignacio Romero‐Muñiz | Edward Loukopoulos | Ying Xiong | Félix Zamora | Ana E. Platero‐Prats
Source: Chemical Society Reviews (review, 2024-01-01)
Field: Chemistry — Inorganic Chemistry

Exploring porous structures without crystals: advancements with pair distribution function in metal- and covalent organic frameworks The pair distribution function (PDF) is a versatile characterisation tool in materials science, capable of retrieving atom-atom distances on a continuous scale (from a few angstroms to nanometres), without being restricted to crystalline samples. Typically, total scattering experiments are performed using high-energy synchrotron X-rays, neutrons or electrons to achieve a high atomic resolution in a short time. Recently, PDF analysis provides a powerful approach to target current characterisation challenges in the field of metal- and covalent organic frameworks. By identifying molecular interactions on the pore surfaces, tracking complex structural transformations involving disorder states, and elucidating nucleation and growth mechanisms, structural analysis using PDF has provided invaluable insights into these materials. This review article highlights the significance of PDF analysis in advancing our understanding of MOFs and COFs, paving the way for innovative applications and discoveries in porous materials research. Covalent bond ; Nucleation ; Pair distribution function ; Metal-organic framework ; Porosity ; Function (biology) ; Materials science ; Metal ; Nanotechnology ; Chemistry ; Chemical physics ; Physical chemistry ; Organic chemistry ; Physics ; Metallurgy ; Composite material ; Adsorption Metal-Organic Frameworks: Synthesis and Applications ; Covalent Organic Framework Applications ; X-ray Diffraction in Crystallography
Authors: Yaolan Niu | Wei Hu
Source: Sustainable Environment Research (review, 2024-11-25)
Field: Materials Science — Biomaterials

Preparation, characterization and application in environmental protection of low-molecular-weight chitosan: a review Abstract Chitosan is a biopolymer derived from chitin, which is the second most abundant and renewable polymer in nature after cellulose. Low-molecular-weight chitosan (LMWC) is the degradation product of chitosan through depolymerization. Compared with conventional chitosan, LMWC is considered as one of the most promising functional materials due to its characteristics of lower polymerization degree, lower viscosity, good water solubility, reactivity and degradability. This review focused on the preparation and characterization methods as well as the application in environmental remediation of LMWC. The three main methods of LMWC preparation including chemical, physical and enzymatic methods were summarized and compared in this paper. The mechanism, advantages and disadvantages of various preparation methods were also discussed. In addition, the applications of LMWC in environmental fields such as water treatment, soil remediation and air purification were briefly reviewed. With the continuous progress of science and technology and the improvement of environmental awareness, it is believed that more efficient, economical and environmentally friendly chitosan degradation methods will be developed, providing strong support for the wide application of LMWC in the field of environmental protection. Chitosan ; Depolymerization ; Biopolymer ; Environmental remediation ; Environmentally friendly ; Cellulose ; Chemistry ; Polymer ; Organic chemistry ; Contamination Nanocomposite Films for Food Packaging ; Microplastics and Plastic Pollution ; Nanoparticles: synthesis and applications
Authors: Katalin Balázsi | Alaa Almansoori | Csaba Balázsi
Source: Ceramics (article, 2024-11-20)
Field: Materials Science — Ceramics and Composites

Superior Ceramics: Graphene and Carbon Nanotube (CNT) Reinforcements Carbon nanotube (CNT)/graphene ceramic composites with outstanding properties are expected to replace a number of components currently used in the automotive and aerospace industries in the future. Consequently, this area of research has progressed significantly. This review paper, therefore, delves into the enhancement of ceramic properties through the integration of graphene and CNTs. These reinforcements are known to mitigate the inherent brittleness of ceramics, thereby unlocking their potential for applications in sectors requiring high mechanical reliability, such as the aerospace, automotive, and biomedical industries. By summarizing recent research, this paper outlines various preparation methods, including ball milling, heat pressing and spark plasma sintering, and discusses how these techniques contribute to improved mechanical and thermal performance. This review emphasizes the critical role of graphene and CNT ratios, sizes, and their synergistic effects in enhancing fracture toughness, machinability, and overall structural integrity. Thus, this paper provides a comprehensive overview of the current research in this area and discusses the potential of these technologies. Materials science ; Carbon nanotube ; Graphene ; Ceramic ; Spark plasma sintering ; Aerospace ; Machinability ; Fracture toughness ; Automotive industry ; Pressing ; Nanotechnology ; Brittleness ; Toughness ; Composite material ; Machining ; Engineering ; Metallurgy ; Aerospace engineering Advanced ceramic materials synthesis ; Graphene research and applications ; Advanced materials and composites
Authors: Elaheh Mohebbi | Eleonora Pavoni | Luca Pierantoni | Pierluigi Stipa | Gian Marco Zampa | Emiliano Laudadio | Davide Mencarelli
Source: Results in Physics (article, 2024-02-01)
Field: Materials Science — Materials Chemistry

Band gap and THz optical adsorption of SnSe and SnSe2 nanosheets on graphene: Negative dielectric constant of SnSe Density functional theory (DFT) calculations have been used to investigate physical–chemical sensing of various proposed interfaces as SnSe@Graphene, SnSe2@Graphene, Graphene@SnSe@Graphene, and Graphene@SnSe2@Graphene, where dispersion corrections have been included to taken into account the vdW interactions between the layers. Initially, we predicted the electronic structures, mobility and carrier concentrations (cc) of SnSe and SnSe2 structures. Using different methodology, the outcomes have confirmed the semiconductor properties of SnSe and SnSe2 with indirect bandgap of 1.20 eV and 0.94 eV calculated by Generalized Gradient Approximation (GGA) and MetaGGA (MGGA) adopted with Perdew-Burke-Ernzerhof (PBE) functional, while hybrid Heyd-Scuseria-Ernzerhof (HSE) hybrid functional overestimated the experimental observations for both materials. Room temperature high mobility and cc have predicted by 126 × 103 cm2V−1 s−1 with cc of 1.3 × 1013 cm3 for a two layers SnSe and 69 × 103 cm2V−1 s−1 with cc of 4.2 × 1018 cm3 for three layers SnSe2. Optical absorption spectrum revealed that the presence of two peaks at 60 THz and 48 THz with the intensity of 89879 cm−1 and 34504 cm−1 for interfaces including two layers of graphene. More interestingly, dielectric constant calculations showed that the transfer of carriers between graphene layers and SnSe in Graphene@SnSe@Graphene interface with absorption peak shifted to 24 THz along the in-plane direction and negative dielectric constants in the range of 24–169 THz, showing the effect of light-trapping through plasmonic. Graphene ; Materials science ; Density functional theory ; Dielectric ; Band gap ; Absorption (acoustics) ; Optoelectronics ; Terahertz radiation ; Electron mobility ; Semiconductor ; Plasmon ; Hybrid functional ; Graphene nanoribbons ; Condensed matter physics ; Nanotechnology ; Chemistry ; Computational chemistry ; Physics Graphene research and applications ; 2D Materials and Applications ; Quantum Dots Synthesis And Properties
Authors: Xufan Li | Samuel Wyss | Emanuil Yanev | Qing‐Jie Li | Shuang Wu | Yongwen Sun | Raymond R. Unocic | Joseph Stage | Matthew Strasbourg | Lucas M. Sassi | Yingxin Zhu | Ju Li | Yang Yang | James Hone | Nicholas J. Borys | P. James Schuck | Avetik R. Harutyunyan
Source: Nature Communications (article, 2024-11-21)
Field: Materials Science — Materials Chemistry

Width-dependent continuous growth of atomically thin quantum nanoribbons from nanoalloy seeds in chalcogen vapor Nanoribbons (NRs) of atomic layer transition metal dichalcogenides (TMDs) can boost the rapidly emerging field of quantum materials owing to their width-dependent phases and electronic properties. However, the controllable downscaling of width by direct growth and the underlying mechanism remain elusive. Here, we demonstrate the vapor-liquid-solid growth of single crystal of single layer NRs of a series of TMDs (MeX<sub>2</sub>: Me = Mo, W; X = S, Se) under chalcogen vapor atmosphere, seeded by pre-deposited and respective transition metal-alloyed nanoparticles that also control the NR width. We find linear dependence of growth rate on supersaturation, known as a criterion for continues growth mechanism, which decreases with decreasing of NR width driven by the Gibbs-Thomson effect. The NRs show width-dependent photoluminescence and strain-induced quantum emission signatures with up to ≈ 90% purity of single photons. We propose the path and underlying mechanism for width-controllable growth of TMD NRs for applications in quantum optoelectronics. Chalcogen ; Materials science ; Photoluminescence ; Nanotechnology ; Chemical vapor deposition ; Supersaturation ; Quantum dot ; Optoelectronics ; Condensed matter physics ; Chemical physics ; Chemistry ; Physics ; Thermodynamics ; Crystallography 2D Materials and Applications ; Quantum Dots Synthesis And Properties ; ZnO doping and properties
Authors: Ihor Bordun | Ewelina Szymczykiewicz
Source: Applied Sciences (article, 2024-01-12)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Synthesis and Electrochemical Properties of Fe3O4/C Nanocomposites for Symmetric Supercapacitors In the work, nanostructured Fe3O4/C composites based on natural raw materials (beet pulp and corn stalks) are synthesised in various ways. Iron chloride FeCl3 is used as an activator. The synthesised composites are investigated using the following methods, scanning electron microscopy, X-ray diffractometry, nitrogen adsorption/desorption porometry, magnetometry, impedance and galvanostatic measurements. The presence of nanosized Fe3O4 magnetite in the synthesised carbon structures is disclosed. Based on the magnetic measurements, the particle size of Fe3O4 is on average 50 nm for the sample of the composite synthesised from beet pulp in one step, 30 nm for the sample of the composite synthesised from beet pulp in two steps, 33 nm for the composite synthesised from corn stalks in one step, and 29 nm for the composite synthesised from corn stalks in two steps. It is shown that the two-step synthesis using pre-carbonised raw materials gives the specific capacitance of the composite made with beet pulp at 96 F/g, and for the composite made with corn stalks at 95 F/g. The high coulombic efficiency (&gt;95%) and the stability of the electrical capacitance during long-term cycling make it possible to use Fe3O4/C composites for the manufacture of supercapacitor electrodes with aqueous electrolytes. Materials science ; Supercapacitor ; Composite number ; Nanocomposite ; Pulp (tooth) ; Adsorption ; Chemical engineering ; Composite material ; Electrochemistry ; Electrode ; Chemistry ; Organic chemistry Supercapacitor Materials and Fabrication ; Advanced Theoretical and Applied Studies in Material Sciences and Geometry ; Aerogels and thermal insulation
Authors: Yanling Liu | Shouzhi Yang | Shunxiang Li | Yuning Wang | Xiaohui Liu | Wei Xu | Haiyang Su | Kun Qian
Source: Small (review, 2024-12-12)
Field: Chemistry — Spectroscopy

Noble Metal Nanoparticle Assisted Mass Spectrometry for Metabolite‐Based In Vitro Diagnostics In vitro diagnostics (IVD) makes clinical diagnosis rapid, simple, and noninvasive to patients, playing a crucial role in the early diagnosis and monitoring of diseases. Metabolic biomarkers are closely correlated to the phenotype of diseases. However, most IVD platforms are constrained by the sensitivity and throughput of assay. In recent years, noble-metal-nanoparticle (NMNP)-assisted laser desorption/ionization mass spectrometry (LDI MS) has generated major advances in metabolite analysis, significantly improving the sensitivity, accuracy, and throughput of IVD due to the unique optical and electrical properties of NMNPs. This review systematically assesses the development of NMNPs as LDI MS matrices in the detection of metabolites for IVD application. The analysis of several NMNP structures, such as core-shell, porous, and 2D nanoparticles, elucidates their significant contribution to the enhancement of MS performance. Furthermore, the recent advancements in the application of NMNPs for diagnosing various systemic diseases are summarized. Finally, the prospects and challenges of NMNP-assisted MS for IVD are discussed. This review elucidates the roles of NMNPs' structure in enhancing MS-based metabolic detection and provides an overview of various IVD applications, consequently offering comprehensive insights for researchers and developers in this field. Nanotechnology ; Nanoparticle ; Mass spectrometry ; Metabolite ; Metabolomics ; Materials science ; Chemistry ; Computational biology ; Biology ; Chromatography ; Biochemistry Mass Spectrometry Techniques and Applications ; Advanced Proteomics Techniques and Applications ; Metabolomics and Mass Spectrometry Studies
Authors: Volker Blüm | Ryoji Asahi | Jochen Autschbach | Christoph Bannwarth | Gustav Bihlmayer | Stefan Blügel | Lori A. Burns | T. Daniel Crawford | William Harbutt Dawson | Wibe A. de Jong | Claudia Draxl | Claudia Filippi | Luigi Genovese | Paolo Giannozzi | Niranjan Govind | Sharon Hammes‐Schiffer | Jeff R. Hammond | B. Hourahine | Anubhav Jain | Yosuke Kanai | Paul R. C. Kent | Ask Hjorth Larsen | Susi Lehtola | Xiaosong Li | Roland Lindh | Satoshi Maeda | Nancy Makri | Jonathan E. Moussa | Takahito Nakajima | Jessica A. Nash | Micael J. T. Oliveira | Pansy D. Patel | Giovanni Pizzi | Geoffrey Pourtois | Benjamin P. Pritchard | Eran Rabani | Markus Reiher | Lucia Reining | Xinguo Ren | Mariana Rossi | H. Bernhard Schlegel | Nicola Seriani | Lyudmila V. Slipchenko | Alexander Thom | Edward F. Valeev | Benoît Van Troeye | Lucas Visscher | Vojtěch Vlček | Hans‐Joachim Werner | David B. Williams‐Young | Theresa L. Windus
Source: Electronic Structure (article, 2024-05-08)
Field: Materials Science — Materials Chemistry

Roadmap on methods and software for electronic structure based simulations in chemistry and materials Abstract This Roadmap article provides a succinct, comprehensive overview of the state of electronic structure (ES) methods and software for molecular and materials simulations. Seventeen distinct sections collect insights by 51 leading scientists in the field. Each contribution addresses the status of a particular area, as well as current challenges and anticipated future advances, with a particular eye towards software related aspects and providing key references for further reading. Foundational sections cover density functional theory and its implementation in real-world simulation frameworks, Green’s function based many-body perturbation theory, wave-function based and stochastic ES approaches, relativistic effects and semiempirical ES theory approaches. Subsequent sections cover nuclear quantum effects, real-time propagation of the ES, challenges for computational spectroscopy simulations, and exploration of complex potential energy surfaces. The final sections summarize practical aspects, including computational workflows for complex simulation tasks, the impact of current and future high-performance computing architectures, software engineering practices, education and training to maintain and broaden the community, as well as the status of and needs for ES based modeling from the vantage point of industry environments. Overall, the field of ES software and method development continues to unlock immense opportunities for future scientific discovery, based on the growing ability of computations to reveal complex phenomena, processes and properties that are determined by the make-up of matter at the atomic scale, with high precision. Software ; Engineering physics ; Computer science ; Chemistry ; Engineering ; Materials science ; Operating system Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Electronic and Structural Properties of Oxides