jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Wenhe Luo | Fengliang Dong | Meng Wang | Tao Li | Yun Wang | Wanqin Dai | Junzhe Zhang | Chunlei Jiao | Zhuda Song | Jiaqi Shen | Yuhui Ma | Yayun Ding | Fang Yang | Zhiyong Zhang | Xiao He
Source: Analytical Chemistry (article, 2023-04-05)
Field: Chemistry — Analytical Chemistry

Particulate Standard Establishment for Absolute Quantification of Nanoparticles by LA-ICP-MS The development of nanotechnology has transformed many cutting-edge studies related to single-molecule analysis into nanoparticle (NP) detection with a single-NP sensitivity and ultrahigh resolution. While laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS) has been successful in quantifying and tracking NPs, its quantitative calibration remains a major challenge due to the lack of suitable standards and the uncertain matrix effects. Herein, we frame a new approach to prepare quantitative standards via precise synthesis of NPs, nanoscale characterization, on-demand NP distribution, and deep learning-assisted NP counting. Gold NP standards were prepared to cover the mass range from sub-femtogram to picogram levels with sufficient accuracy and precision, thus establishing an unambiguous relationship between the sampled NP number in each ablation and the corresponding mass spectral signal. Our strategy facilitated for the first time the study of the factors affecting particulate sample capture and signal transductions in LA-ICP-MS analysis and culminated in the development of an LA-ICP-MS-based method for absolute NP quantification with single-NP sensitivity and single-cell quantification capability. The achievements would herald the emergence of new frontiers cut across a spectrum of toxicological and diagnostic issues related to NP quantification. Chemistry ; Inductively coupled plasma mass spectrometry ; Nanoparticle ; Resolution (logic) ; Mass spectrometry ; Analytical Chemistry (journal) ; Nanotechnology ; Chromatography Analytical chemistry methods development ; Biosensors and Analytical Detection ; Gold and Silver Nanoparticles Synthesis and Applications
Authors: Saeed Moayedpour | Imanuel Bier | Wen Wen | Derek Dardzinski | Olexandr Isayev | Noa Marom
Source: The Journal of Physical Chemistry C (article, 2023-05-22)
Field: Materials Science — Materials Chemistry

Structure Prediction of Epitaxial Organic Interfaces with Ogre, Demonstrated for Tetracyanoquinodimethane (TCNQ) on Tetrathiafulvalene (TTF) Highly ordered epitaxial interfaces between organic semiconductors are considered as a promising avenue for enhancing the performance of organic electronic devices including solar cells and transistors, thanks to their well-controlled, uniform electronic properties and high carrier mobilities. The electronic structure of epitaxial organic interfaces and their functionality in devices are inextricably linked to their structure. We present a method for structure prediction of epitaxial organic interfaces based on lattice matching followed by surface matching, implemented in the open-source Python package, Ogre. The lattice matching step produces domain-matched interfaces, where commensurability is achieved with different integer multiples of the substrate and film unit cells. In the surface matching step, Bayesian optimization (BO) is used to find the interfacial distance and registry between the substrate and film. The BO objective function is based on dispersion corrected deep neural network interatomic potentials. These are shown to be in qualitative agreement with density functional theory (DFT) regarding the optimal position of the film on top of the substrate and the ranking of putative interface structures. Ogre is used to investigate the epitaxial interface of 7,7,8,8-tetracyanoquinodimethane (TCNQ) on tetrathiafulvalene (TTF), whose electronic structure has been probed by ultraviolet photoemission spectroscopy (UPS), but whose structure had been hitherto unknown [Organic Electronics 2017, 48, 371]. We find that TCNQ(001) on top of TTF(100) is the most stable interface configuration, closely followed by TCNQ(010) on top of TTF(100). The density of states, calculated using DFT, is in excellent agreement with UPS, including the presence of an interface charge transfer state. Tetrathiafulvalene ; Materials science ; Density functional theory ; Epitaxy ; Organic semiconductor ; Tetracyanoquinodimethane ; Electronic structure ; Photoemission spectroscopy ; Optoelectronics ; Chemical physics ; Crystallography ; Computational chemistry ; Nanotechnology ; Chemistry ; X-ray photoelectron spectroscopy ; Molecule ; Physics ; Organic chemistry Machine Learning in Materials Science ; Organic and Molecular Conductors Research ; Perovskite Materials and Applications
Authors: Diane Abderrahim | Taoufiq Saffaj | Bouchaîb Ihssane | Reda Rabie
Source: Chemometrics and Intelligent Laboratory Systems (article, 2023-07-27)
Field: Chemistry — Analytical Chemistry

Enhancing tomato leaf nitrogen analysis through portable NIR spectrometers combined with machine learning and chemometrics Chemometrics ; Partial least squares regression ; Principal component analysis ; Preprocessor ; Computer science ; Data pre-processing ; Spectrometer ; Machine learning ; Process engineering ; Artificial intelligence ; Biological system ; Engineering Spectroscopy and Chemometric Analyses ; Remote Sensing in Agriculture ; Leaf Properties and Growth Measurement
Authors: Nichenametla Jai Sai | Punit Rathore | Kumar Sridharan | Ankur Chauhan
Source: Fusion Engineering and Design (article, 2023-08-21)
Field: Materials Science — Materials Chemistry

Machine learning-based predictions of yield strength for neutron-irradiated ferritic/martensitic steels Materials science ; Support vector machine ; Gradient boosting ; Random forest ; Boosting (machine learning) ; Ultimate tensile strength ; Irradiation ; Yield (engineering) ; Machine learning ; Artificial intelligence ; Neutron ; Computer science ; Algorithm ; Composite material ; Nuclear physics ; Physics Fusion materials and technologies ; Nuclear Materials and Properties ; Hydrogen embrittlement and corrosion behaviors in metals
Authors: Aqeel Ahmad | Nurlidia Mansor | Hamayoun Mahmood | Faiza Sharif | Rizwan Safdar | Muhammad Moniruzzaman
Source: Journal of Applied Polymer Science (article, 2023-01-25)
Field: Materials Science — Polymers and Plastics

Evaluation thermal degradation kinetics of ionic liquid assisted polyetheretherketone‐multiwalled carbon nanotubes composites Abstract The incorporation of multiwalled carbon nanotubes (MWCNT) into polyetheretherketone (PEEK) composites has emerged as a promising strategy for enhancing the thermomechanical characteristics of PEEK composite materials. This study investigates the thermal behavior and kinetics prediction of PEEK/MWCNT composites comprising different ionic liquids (ILs), namely 1‐butyl‐3‐methylimidazolium hydrogen sulfate ([BMIM]HSO 4 ), 1‐butyl‐3‐methylimidazolium acetate ([BMIM]Ac), 1‐ethyl‐3‐methylimidazolium acetate ([EMIM]Ac) and 1‐ethyl‐3‐methylimidazolium hydrogen sulfate ([EMIM]HSO 4 ). Three non‐isothermal methods Coats‐Redfern, Broido, and Horowitz‐Metzger, were employed to model the thermal decomposition profiles of fabricated composites to calculate the activation energy. The highest decomposition temperature (580°C) was obtained for [BMIM]HSO 4 ‐based PEEK/MWCNT composites. Moreover, a 3%–8% increase in the activation energy was obtained compared to PEEK/MWCNT manufactured without ILs. The Coats‐Redfern model was superior to Broido and Horowitz‐Metzger models in modeling the thermal degradation of developed composites, as evidenced from the higher value of the coefficient of determination (R 2 ≥ 0.9899). By determining the Root Mean Square Error (RMSE) and R 2 for the thermal degradation kinetics data, the artificial neural network (ANN) model was employed. The ANN model accurately predicted the mass loss curves, exhibiting R 2 ≥ 0.9815 for the designed model. These findings can assist in establishing an IL‐assisted benign approach for PEEK/MWCNT/IL composites with superior thermal characteristics. Peek ; Ionic liquid ; Materials science ; Composite material ; Activation energy ; Thermal stability ; Composite number ; Thermal decomposition ; Isothermal process ; Kinetics ; Chemical engineering ; Catalysis ; Physical chemistry ; Organic chemistry ; Chemistry ; Polymer ; Thermodynamics Polymer crystallization and properties ; Polymer Nanocomposites and Properties ; Carbon dioxide utilization in catalysis
Authors: Qitong Zhong | Xing Tan | Ruixing Du | Jia‐Cheng Liu | Longfei Liao | Cheng Wang | Ruiyan Sun | Zhenchen Tang | Jie Ren | Chalachew Mebrahtu | Feng Zeng
Source: Preprints.org (preprint, 2023-02-17)
Field: Materials Science — Materials Chemistry

Is ChatGPT a Reliable Source for Writing Review Articles in Catalysis Research? A Case Study on CO2 Hydrogenation to Higher Alcohols ChatGPT is an AI language model trained on vast amounts of text data, including scientific papers, providing a comprehensive understanding of catalysis. However, its reliability in catalysis research is unknown. To evaluate reliability, we compared a ChatGPT-generated review article on heterogeneous catalysts for higher alcohols synthesis by CO2 hydrogenation to published peer-reviewed papers. Although the ChatGPT review article covers most necessary parts, it lacks sufficient discussion of the reaction mechanism. The core sections are too general, being not specific enough to the topic, and contain errors. The lack of citations further increases unreliability. While ChatGPT can provide much content on catalysis, it is insufficient and inaccurate for research on specific topics. Reliability (semiconductor) ; Catalysis ; Mechanism (biology) ; Computer science ; Chemistry ; Data science ; Organic chemistry ; Epistemology ; Philosophy ; Physics Machine Learning in Materials Science ; Topic Modeling
Authors: Ali Hashemi | Sana Bougueroua | Marie‐Pierre Gaigeot | Evgeny A. Pidko
Source: Journal of Chemical Information and Modeling (article, 2023-09-22)
Field: Materials Science — Materials Chemistry

HiREX: High-Throughput Reactivity Exploration for Extended Databases of Transition-Metal Catalysts A method is introduced for the automated analysis of reactivity exploration for extended in silico databases of transition-metal catalysts. The proposed workflow is designed to tackle two key challenges for bias-free mechanistic explorations on large databases of catalysts: (1) automated exploration of the chemical space around each catalyst with unique structural and chemical features and (2) automated analysis of the resulting large chemical data sets. To address these challenges, we have extended the application of our previously developed ReNeGate method for bias-free reactivity exploration and implemented an automated analysis procedure to identify the classes of reactivity patterns within specific catalyst groups. Our procedure applied to an extended series of representative Mn(I) pincer complexes revealed correlations between structural and reactive features, pointing to new channels for catalyst transformation under the reaction conditions. Such an automated high-throughput virtual screening of systematically generated hypothetical catalyst data sets opens new opportunities for the design of high-performance catalysts as well as an accelerated method for expert bias-free high-throughput in silico reactivity exploration. Reactivity (psychology) ; Workflow ; Computer science ; Chemical space ; Throughput ; Catalysis ; Key (lock) ; Virtual screening ; Database ; Chemistry ; Data mining ; Computational chemistry ; Molecular dynamics ; Drug discovery Machine Learning in Materials Science ; Asymmetric Hydrogenation and Catalysis ; Catalysis and Oxidation Reactions
Authors: Yas Al‐Hadeethi | Chandraman Patil | Wafa Said Bait Haridh | Moustafa Ahmed | Jamaan E. Alassafi | Nada M. Bedaiwi | Elham Heidari | Hamed Dalir
Source: ArXiv.org (preprint, 2023-10-05)
Field: Materials Science — Materials Chemistry

Towards Developing Mid-Infrared Photonics Using Mxenes Recent research and development in the mid-infrared (IR) wavelength range (2-20 um) for a variety of applications, such as trace gas monitoring, thermal imaging, and free space communications have shown tremendous and fascinating progress. MXenes, which mainly refer to two-dimensional (2D) transition-metal carbides, nitrides, and carbonitrides, have drawn a lot of interest since their first investigation in 2011. MXenes project enormous potential for use in optoelectronics, photonics, catalysis, and energy harvesting fields proven by extensive experimental and theoretical studies over a decade. MXenes offers a novel 2D nano platform for cutting-edge optoelectronics devices due to their interesting mechanical, optical, and electrical capabilities, along with their elemental and chemical composition. We here discuss the key developments of MXene emphasizing the evolution of material synthesis methods over time and the resulting device applications. Photonic and optoelectronic device design and fabrication for mid-IR photonics are demonstrated by integrating MXene materials with various electrical and photonic platforms. Here, we show the potential of using Mxene in photonics for mid-IR applications and a pathway toward achieving next-generation devices for various applications. MXenes ; Photonics ; Mid infrared ; Materials science ; Nanotechnology ; Fabrication ; Infrared ; Nitride ; Optoelectronics ; Engineering physics ; Engineering ; Wavelength ; Physics ; Optics ; Layer (electronics) MXene and MAX Phase Materials ; 2D Materials and Applications ; Advanced Memory and Neural Computing
Authors: Zhu Liang | Matthew R. Carbone | Wei Chen | Fanchen Meng | Eli Stavitski | Deyu Lu | Mark S. Hybertsen | Xiaohui Qu
Source: Physical Review Materials (article, 2023-05-16)
Field: Materials Science — Materials Chemistry

Decoding structure-spectrum relationships with physically organized latent spaces A semisupervised machine learning method for the discovery of structure-spectrum relationships is developed and then demonstrated using the specific example of interpreting x-ray absorption near-edge structure (XANES) spectra. This method constructs a one-to-one mapping between individual structure descriptors and spectral trends. Specifically, an adversarial autoencoder is augmented with a rank constraint (RankAAE). The RankAAE methodology produces a continuous and interpretable latent space, where each dimension can track an individual structure descriptor. As a part of this process, the model provides a robust and quantitative measure of the structure-spectrum relationship by decoupling intertwined spectral contributions from multiple structural characteristics. This makes it ideal for spectral interpretation and the discovery of descriptors. The capability of this procedure is showcased by considering five local structure descriptors and a database of &gt;50 000 simulated XANES spectra across eight first-row transition metal oxide families. The resulting structure-spectrum relationships not only reproduce known trends in the literature but also reveal unintuitive ones that are visually indiscernible in large datasets. The results suggest that the RankAAE methodology has great potential to assist researchers in interpreting complex scientific data, testing physical hypotheses, and revealing patterns that extend scientific insight. Computer science ; Autoencoder ; Artificial intelligence ; Pattern recognition (psychology) ; Subspace topology ; Deep learning Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Metabolomics and Mass Spectrometry Studies
Authors: Claire Förster | Annette Andrieu‐Brunsen
Source: Chemical Communications (review, 2023-01-01)
Field: Chemistry — Organic Chemistry

Recent developments in visible light induced polymerization towards its application to nanopores Visible light induced polymerizations are a strongly emerging field in recent years. Besides the often mild reaction conditions, visible light offers advantages of spatial and temporal control over chain growth, which makes visible light ideal for functionalization of surfaces and more specifically of nanoscale pores. Current challenges in nanopore functionalization include, in particular, local and highly controlled polymer functionalizations. Using spatially limited light sources such as lasers or near field modes for light-induced polymer functionalization is envisioned to allow local functionalization of nanopores and thereby improve nanoporous material performance. These light sources are usually providing visible light while classical photopolymerizations are mostly based on UV-irradiation. In this review, we highlight developments in visible light induced polymerizations and especially in visible light induced controlled polymerizations as well as their potential for nanopore functionalization. Existing examples of visible light induced polymerizations in nanopores are emphasized. Nanopore ; Polymerization ; Visible spectrum ; Nanotechnology ; Materials science ; Chemistry ; Polymer ; Optoelectronics ; Composite material Advanced Polymer Synthesis and Characterization ; Advanced Photocatalysis Techniques ; Luminescence and Fluorescent Materials
Authors: Pieter Janssens | Jeroen Poissonnier | Anoop Chakkingal | René Bos | Joris Thybaut
Source: Catalysis Communications (article, 2023-05-08)
Field: Materials Science — Materials Chemistry

Recent advances in the use of Steady-State Isotopic Transient Kinetic Analysis data in (micro)kinetic modeling for catalyst and process design Developing new catalysts with improved performance is crucial for establishing a sustainable chemical industry and requires atomic-scale knowledge of the relevant catalyst-species interactions. A powerful experimental technique to gather such detailed knowledge is Steady-State Isotopic Transient Kinetic Analysis (SSITKA). By combining SSITKA with (micro)kinetic modeling, the observed catalyst performance can be rationalized to advance the development of structure-activity relationships, and to obtain fundamental insights in complex reaction pathways. In turn, tailored experimental and computational methodologies provide the tools to access this detailed information. This mini review summarizes the developments that have been made in these fields during the last decade. Transient (computer programming) ; Kinetic energy ; Catalysis ; Steady state (chemistry) ; Process (computing) ; Biochemical engineering ; Nanotechnology ; Chemistry ; Atomic units ; Computer science ; Process engineering ; Chemical physics ; Materials science ; Physics ; Physical chemistry ; Engineering ; Organic chemistry Catalytic Processes in Materials Science ; Catalysis and Oxidation Reactions ; Catalysts for Methane Reforming
Authors: Boyu Zhao | Paul Wilson
Source: Polymer Chemistry (article, 2023-01-01)
Field: Chemistry — Organic Chemistry

Recent progress and applications enabled <i>via</i> electrochemically triggered and controlled chain-growth polymerizations Electrochemical chain-growth polymerization has emerged as a powerful tool in polymer chemistry. Recent progress made in radical, ionic and ring-opening polymerizations and the applications enabled by use of an electric field to perform such reactions is reported. Polymerization ; Electrochemistry ; Polymer chemistry ; Polymer ; Nanotechnology ; Ionic bonding ; Ionic liquid ; Materials science ; Chemistry ; Polymer science ; Organic chemistry ; Catalysis ; Ion ; Electrode ; Physical chemistry Advanced Polymer Synthesis and Characterization ; Machine Learning in Materials Science ; Conducting polymers and applications
Authors: Wenmiao Chen | Peiyu Cai | Hong‐Cai Zhou | Sherzod T. Madrahimov
Source: Angewandte Chemie (article, 2023-12-23)
Field: Chemistry — Inorganic Chemistry

Bridging Homogeneous and Heterogeneous Catalysis: Phosphine‐Functionalized Metal‐Organic Frameworks Abstract Phosphine‐functionalized metal‐organic frameworks (P‐MOFs) as an emerging class of coordination polymers, have provided novel opportunities for the development of heterogeneous catalysts. Yet, compared with the ubiquitous phosphine systems in homogeneous catalysis, heterogenization of phosphines in MOFs is still at its early stage. In this Minireview, we summarize the synthetic strategies, characterization and catalytic reactions based on the P‐MOFs reported in literature. In particular, various catalytic reactions are discussed in detail in terms of phosphine ligand structure‐function relationship, including the potential obstacles for future development. Finally, we discuss the possible solutions, including new types of reactions and techniques as the perspectives for the development of P‐MOF catalysts, highlighting the opportunities and challenges. Phosphine ; Homogeneous ; Metal-organic framework ; Catalysis ; Bridging (networking) ; Homogeneous catalysis ; Chemistry ; Nanotechnology ; Heterogeneous catalysis ; Combinatorial chemistry ; Materials science ; Organic chemistry ; Computer science ; Physics Metal-Organic Frameworks: Synthesis and Applications ; Asymmetric Hydrogenation and Catalysis ; Chemical Synthesis and Reactions
Authors: Matthew S. Johnson | Hao‐Wei Pang | A. Mark Payne | William H. Green
Source: ChemRxiv (preprint, 2023-04-18)
Field: Chemistry — Physical and Theoretical Chemistry

ReactionMechanismSimulator.jl: A Modern Approach to Chemical Kinetic Mechanism Simulation and Analysis We present ReactionMechanismSimulator.jl (RMS), a modern differentiable software for simulation and analysis of chemical kinetic mechanisms including multi-phase systems. RMS has already been applied to problems in combustion, pyrolysis, polymers, pharmaceuticals, catalysis, and electrocatalysis. RMS is written in Julia, making it much easier to develop and allowing it to take advantage of Julia’s extensive numerical computing ecosystem. In addition to its extensive library of optimized analytic Jacobians, RMS can generate and use Jacobians computed using automatic differentiation and symbolically generated analytic Jacobians. RMS is demonstrated to be faster than Cantera and Chemkin in several benchmarks. RMS also implements an extensive set of features for analyzing chemical mechanisms, including a library of easy to call plotting functions, molecular structure resolved flux diagram generation, crash analysis, traditional sensitivity analysis, transitory sensitivity analysis and an automatic mechanism analysis toolkit. RMS implements efficient adjoint and parallel forward sensitivity analyses. We also demonstrate the ease of adding new features to RMS. Sensitivity (control systems) ; Computer science ; Software ; Set (abstract data type) ; Algorithm ; Root mean square ; Computational science ; Biological system ; Programming language ; Electronic engineering ; Physics Advanced Physical and Chemical Molecular Interactions ; Advanced Chemical Physics Studies ; Quantum chaos and dynamical systems