jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Liang Ge | Chi Zhang | Chengkai Pan | Dingxing Wang | Dongying Liu | Zhiqiang Li | Pingkang Shen | Lifang Tian | Chao Feng
Source: Nature Communications (article, 2022-10-08)
Field: Chemistry — Organic Chemistry

Photoredox-catalyzed C–C bond cleavage of cyclopropanes for the formation of C(sp3)–heteroatom bonds Sterically congested C-O and C-N bonds are ubiquitous in natural products, pharmaceuticals, and bioactive compounds. However, the development of a general method for the efficient construction of those sterically demanding covalent bonds still remains a formidable challenge. Herein, a photoredox-driven ring-opening C(sp<sup>3</sup>)-heteroatom bond formation of arylcyclopropanes is presented, which enables the construction of structurally diversified while sterically congested dialkyl ether, alkyl ester, alcohol, amine, chloride/fluoride, azide and also thiocyanate derivatives. The selective single electron oxidation of aryl motif associated with the thermodynamic driving force from ring strain-release is the key for this transformation. By this synergistic activation mode, C-C bond cleavage of otherwise inert cyclopropane framework is successfully unlocked. Further mechanistic and computational studies disclose a complete stereoinversion upon nucleophilic attack, thus proving a concerted S<sub>N</sub>2-type ring-opening functionalization manifold, while the regioselectivity is subjected to an orbital control scenario. Chemistry ; Steric effects ; Cyclopropane ; Regioselectivity ; Bond cleavage ; Heteroatom ; Ring strain ; Nucleophile ; Aryl ; Stereochemistry ; Azide ; Single bond ; Ring (chemistry) ; Medicinal chemistry ; Combinatorial chemistry ; Alkyl ; Catalysis ; Organic chemistry Cyclopropane Reaction Mechanisms ; Catalytic C–H Functionalization Methods ; Sulfur-Based Synthesis Techniques
Authors: Lida Hashemi | Mohammad Yaser Masoomi | Hermenegildo Garcı́a
Source: Coordination Chemistry Reviews (article, 2022-08-29)
Field: Chemistry — Inorganic Chemistry

Regeneration and reconstruction of metal-organic frameworks: Opportunities for industrial usage Metal-organic framework ; Regeneration (biology) ; Adsorption ; Chemistry ; Biochemical engineering ; Nanotechnology ; Process engineering ; Desorption ; Scale (ratio) ; Materials science ; Organic chemistry ; Engineering Metal-Organic Frameworks: Synthesis and Applications ; Covalent Organic Framework Applications ; Adsorption and biosorption for pollutant removal
Authors: Mohammed H. Nahari | Amer Al Ali | Abdulaziz Asiri | Mater H. Mahnashi | Ibrahim Ahmed Shaikh | Arun K. Shettar | Joy H. Hoskeri
Source: Nanomaterials (article, 2022-07-14)
Field: Materials Science — Materials Chemistry

Green Synthesis and Characterization of Iron Nanoparticles Synthesized from Aqueous Leaf Extract of Vitex leucoxylon and Its Biomedical Applications The cold extraction method was used to obtain the aqueous extract of <i>Vitex leucoxylon</i> leaves in a ratio of 1:10. Iron nanoparticles (FeNPs) were synthesized using aqueous leaf extract of <i>V. leucoxylon</i> as a reducing agent. The phytoreducing approach was used to make FeNPs by mixing 1 mL of plant extract with 1 mM of ferric sulfate. Scanning electron microscopy (SEM), Fourier-transform infrared spectroscopy (FTIR), Ultraviolet-visible spectroscopy (UV-Vis), and energy-dispersive X-ray spectroscopy were used to examine the synthesized FeNPs. The reducing reaction was shown by a change in the color of the solution, and the formation of black color confirms that FeNPs have been formed. The greatest absorption peak (max) was found at 395 nm in UV-Vis spectral analysis. The FTIR spectra of <i>V. leucoxylon</i> aqueous leaf extract showed shifts in some peaks, namely 923.96 cm<sup>-1</sup> and 1709.89 cm<sup>-1</sup>, with functional groups carboxylic acids, unsaturated aldehydes, and ketones, which were lacking in the FTIR spectra of FeNPs and are responsible for FeNPs formation. FeNPs with diameters between 45 and 100 nm were observed in SEM images. The creation of FeNPs was confirmed by EDX, which shows a strong signal in the metallic iron region at 6-8 Kev. XRD revealed a crystalline nature and an average diameter of 136.43 nm. Antioxidant, anti-inflammatory, cytotoxic, and wound healing in vitro tests reported significant activity of the FeNPs. The cumulative findings of the present study indicate that the green synthesis of FeNPs boosts its biological activity and may serve as a possible dermal wound-healing agent and cytotoxic agent against cancer. Future study is needed on the identification of mechanisms involved in the synthesis of FeNPs by <i>V. leucoxylon</i> and its biomedical applications. Fourier transform infrared spectroscopy ; Aqueous solution ; Nuclear chemistry ; Ferric ; Scanning electron microscope ; Nanoparticle ; Spectroscopy ; Materials science ; Absorption spectroscopy ; Silver nanoparticle ; Infrared spectroscopy ; Analytical Chemistry (journal) ; Chemistry ; Nanotechnology ; Chromatography ; Chemical engineering ; Organic chemistry Nanoparticles: synthesis and applications ; Heavy Metals in Plants
Authors: Bo Yang | Yu‐Lin Li | Jiale Li | Hongchun Shu | Xinyu Zhao | Yaxing Ren | Qiang Li
Source: Protection and Control of Modern Power Systems (review, 2022-09-06)
Field: Materials Science — Materials Chemistry

Comprehensive summary of solid oxide fuel cell control: a state-of-the-art review Abstract Hydrogen energy is a promising renewable resource for the sustainable development of society. As a key member of the fuel cell (FC) family, the solid oxide fuel cell (SOFC) has attracted a lot of attention because of characteristics such as having various sources as fuel and high energy conversion efficiency, and being pollution-free. SOFC is a highly coupled, nonlinear, and multivariable complex system, and thus it is very important to design an appropriate control strategy for an SOFC system to ensure its safe, reliable, and efficient operation. This paper undertakes a comprehensive review and detailed summary of the state-of-the-art control approaches of SOFC. These approaches are divided into eight categories of control: proportional integral differential (PID), adaptive (APC), robust, model predictive (MPC), fuzzy logic (FLC), fault-tolerant (FTC), intelligent and observer-based. The SOFC control approaches are carefully evaluated in terms of objective, design, application/scenario, robustness, complexity, and accuracy. Finally, five perspectives are proposed for future research directions. Solid oxide fuel cell ; Multivariable calculus ; Control engineering ; Robustness (evolution) ; Fuel cells ; Engineering ; Fuzzy logic ; Renewable energy ; Model predictive control ; Nonlinear system ; PID controller ; Computer science ; Control (management) ; Temperature control ; Artificial intelligence Advancements in Solid Oxide Fuel Cells ; Fuel Cells and Related Materials ; Catalytic Processes in Materials Science
Authors: D. Vijay Anand | Qiang Xu | JunJie Wee | Kelin Xia | Tze Chien Sum
Source: npj Computational Materials (article, 2022-09-22)
Field: Materials Science — Materials Chemistry

Topological feature engineering for machine learning based halide perovskite materials design Abstract Accelerated materials development with machine learning (ML) assisted screening and high throughput experimentation for new photovoltaic materials holds the key to addressing our grand energy challenges. Data-driven ML is envisaged as a decisive enabler for new perovskite materials discovery. However, its full potential can be severely curtailed by poorly represented molecular descriptors (or fingerprints). Optimal descriptors are essential for establishing effective mathematical representations of quantitative structure-property relationships. Here we reveal that our persistent functions (PFs) based learning models offer significant accuracy advantages over traditional descriptor based models in organic-inorganic halide perovskite (OIHP) materials design and have similar performance as deep learning models. Our multiscale simplicial complex approach not only provides a more precise representation for OIHP structures and underlying interactions, but also has better transferability to ML models. Our results demonstrate that advanced geometrical and topological invariants are highly efficient feature engineering approaches that can markedly improve the performance of learning models for molecular data analysis. Further, new structure-property relationships can be established between our invariants and bandgaps. We anticipate that our molecular representations and featurization models will transcend the limitations of conventional approaches and lead to breakthroughs in perovskite materials design and discovery. Perovskite (structure) ; Computer science ; Artificial intelligence ; Representation (politics) ; Feature engineering ; Machine learning ; Feature (linguistics) ; Property (philosophy) ; Deep learning ; Topology (electrical circuits) ; Halide ; Theoretical computer science ; Engineering ; Chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Graph Neural Networks
Authors: Carlos Mera Acosta | Elton Ogoshi | J. A. Souza | Gustavo M. Dalpian
Source: ACS Applied Materials & Interfaces (article, 2022-02-08)
Field: Materials Science — Materials Chemistry

Machine Learning Study of the Magnetic Ordering in 2D Materials Magnetic materials have been applied in a large variety of technologies, from data storage to quantum devices. The development of two-dimensional (2D) materials has opened new arenas for magnetic compounds, even when classical theories discourage their examination. Here we propose a machine-learning-based strategy to predict and understand magnetic ordering in 2D materials. This strategy couples the prediction of the existence of magnetism in 2D materials using a random forest and the Shapley additive explanations method with material maps defined by atomic features predicting the magnetic ordering (ferromagnetic or antiferromagnetic). While the random forest model predicts magnetism with an accuracy of 86%, the material maps obtained by the sure independence screening and sparsifying method have an accuracy of ∼90% in predicting the magnetic ordering. Our model indicates that 3d transition metals, halides, and structural clusters with regular transition-metal sublattices have a positive contribution in the total weight deciding the existence of magnetism in 2D compounds. This behavior is associated with the competition between crystal field and exchange splitting. The machine learning model also indicates that the atomic spin orbit coupling (SOC) is a determinant feature for the identification of the patterns separating ferro- from antiferromagnetic order. The proposed strategy is used to identify novel 2D magnetic compounds that, together with the fundamental trends in the chemical and structural space, pave novel routes for experimental exploration. Magnetism ; Antiferromagnetism ; Ferromagnetism ; Materials science ; Paramagnetism ; Condensed matter physics ; Coupling (piping) ; Statistical physics ; Physics 2D Materials and Applications ; Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides
Authors: Grace Dykstra | Benjamin E. Reynolds | Riley Smith | Kai Zhou | Yixin Liu
Source: ACS Applied Materials & Interfaces (article, 2022-05-10)
Field: Chemistry — Analytical Chemistry

Electropolymerized Molecularly Imprinted Polymer Synthesis Guided by an Integrated Data-Driven Framework for Cortisol Detection Molecularly imprinted polymers (MIPs), often called "synthetic antibodies", are highly attractive as artificial receptors with tailored biomolecular recognition to construct biosensors. Electropolymerization is a fast and facile method to directly synthesize MIP sensing elements <i>in situ</i> on the working electrode, enabling ultra-low-cost and easy-to-manufacture electrochemical biosensors. However, due to the high dimensional design space of electropolymerized MIPs (e-MIPs), the development of e-MIPs is challenging and lengthy based on trial and error without proper guidelines. Leveraging machine learning techniques in building the quantitative relationship between synthesis parameters and corresponding sensing performance, e-MIPs' development and optimization can be facilitated. We herein demonstrate a case study on the synthesis of cortisol-imprinted polypyrrole for cortisol detection, where e-MIPs are fabricated with 72 sets of synthesis parameters with replicates. Their sensing performances are measured using a 12-channel potentiostat to construct the subsequent data-driven framework. The Gaussian process (GP) is employed as the mainstay of the integrated framework, which can account for various uncertainties in the synthesis and measurements. The Sobol index-based global sensitivity is then performed upon the GP surrogate model to elucidate the impact of e-MIPs' synthesis parameters on sensing performance and interrelations among parameters. Based on the prediction of the established GP model and local sensitivity analysis, synthesis parameters are optimized and validated by experiment, which leads to remarkable sensing performance enhancement (1.5-fold increase in sensitivity). The proposed framework is novel in biosensor development, which is expandable and also generally applicable to the development of other sensing materials. Molecularly imprinted polymer ; Biosensor ; Materials science ; Sensitivity (control systems) ; Polypyrrole ; Nanotechnology ; Computer science ; Biological system ; Polymer ; Electronic engineering ; Polymerization ; Selectivity ; Chemistry Analytical chemistry methods development ; Electrochemical Analysis and Applications ; Electrochemical sensors and biosensors
Authors: Jesper Byggmästar | K. Nordlund | Flyura Djurabekova
Source: Physical Review Materials (article, 2022-08-11)
Field: Materials Science — Materials Chemistry

Simple machine-learned interatomic potentials for complex alloys Developing data-driven machine-learning interatomic potential for materials containing many elements becomes increasingly challenging due to the vast configuration space that must be sampled by the training data. We study the learning rates and achievable accuracy of machine-learning interatomic potentials for many-element alloys with different combinations of descriptors for the local atomic environments. We show that for a five-element alloy system, potentials using simple low-dimensional descriptors can reach meV/atom-accuracy with modestly sized training datasets, significantly outperforming the high-dimensional SOAP descriptor in data efficiency, accuracy, and speed. In particular, we develop a computationally fast machine-learned and tabulated Gaussian approximation potential (tabGAP) for Mo--Nb--Ta--V--W alloys with a combination of two-body, three-body, and a new simple scalar many-body density descriptor based on the embedded atom method. Simple (philosophy) ; Interatomic potential ; Gaussian ; Atom (system on chip) ; Computer science ; Scalar (mathematics) ; Training set ; Element (criminal law) ; Algorithm ; Artificial intelligence ; Machine learning ; Molecular dynamics ; Computational chemistry ; Mathematics ; Chemistry ; Geometry Machine Learning in Materials Science ; Nuclear Materials and Properties ; Titanium Alloys Microstructure and Properties
Authors: Zhongwei Chen | Boran Yang | Nannan Song | Tingting Chen | Qingwu Zhang | Changxin Li | Juncheng Jiang | Tao Chen | Yuan Yu | Lian X. Liu
Source: Chemical Engineering Journal (article, 2022-11-24)
Field: Materials Science — Polymers and Plastics

Machine learning-guided design of organic phosphorus-containing flame retardants to improve the limiting oxygen index of epoxy resins Limiting oxygen index ; Epoxy ; Limiting ; Fire retardant ; Phosphorus ; Kovats retention index ; Chemistry ; Materials science ; Chemical engineering ; Gas chromatography ; Organic chemistry ; Chromatography ; Mechanical engineering Flame retardant materials and properties ; Synthesis and properties of polymers ; Carbon dioxide utilization in catalysis
Authors: Ning Wang | Baoyin Yuan | Chunmei Tang | Lei Du | Ruijie Zhu | Yoshitaka Aoki | Weibo Wang | Lixin Xing | Siyu Ye
Source: Advanced Materials (article, 2022-09-30)
Field: Materials Science — Materials Chemistry

Machine‐Learning‐Accelerated Development of Efficient Mixed Protonic–Electronic Conducting Oxides as the Air Electrodes for Protonic Ceramic Cells Currently, the development of high-performance protonic ceramic cells (PCCs) is limited by the scarcity of efficient mixed protonic-electronic conducting oxides that can act as air electrodes to satisfy the high protonic conductivity of electrolytes. Despite the extensive research efforts in the past decades, the development of mixed protonic-electronic conducting oxides still remains in a trial-and-error process, which is extremely time consuming and high cost. Herein, based on the data acquired from the published literature, the machine-learning (ML) method is introduced to accelerate the discovery of efficient mixed protonic-electronic conducting oxides. Accordingly, the hydrated proton concentration (HPC) of 3200 oxides is predicted to evaluate the proton conduction that is essential for enhancing the electrochemical performances of PCCs. Subsequently, feature importance for HPC is evaluated to establish a guideline for rapid and accurate design and development of high-efficiency mixed protonic-electronic conducting oxides. Thereafter, screened (La<sub>0.7</sub> Ca<sub>0.3</sub> )(Co<sub>0.8</sub> Ni<sub>0.2</sub> )O<sub>3</sub> (LCCN7382) is prepared, and the experimental HPC adequately corresponds with the predicted results. Moreover, the PCC with LCCN7382 exhibits satisfactory electrochemical performances in electrolysis and fuel cell modes. In addition to the development of a promising air electrode for PCC, this study establishes a new avenue for ML-based development of mixed protonic-electronic conducting oxides. Materials science ; Electrolyte ; Ceramic ; Electrochemistry ; Electrolysis ; Electrode ; Proton ; Conductivity ; Nanotechnology ; Chemical engineering ; Composite material ; Physical chemistry ; Chemistry Advancements in Solid Oxide Fuel Cells ; Fuel Cells and Related Materials ; Gas Sensing Nanomaterials and Sensors
Authors: Samaneh Keshavarz | Oseweuba Valentine Okoro | Masoud Hamidi | Hossein Derakhshankhah | Mehdi Azizi | Seyed Mohammad Nabavi | Shayan Gholizadeh | Seyed Mohammad Amini | Amin Shavandi | Rafael Luque | Hadi Samadian
Source: Coordination Chemistry Reviews (article, 2022-08-23)
Field: Materials Science — Biomaterials

Synthesis, surface modifications, and biomedical applications of carbon nanofibers: Electrospun vs vapor-grown carbon nanofibers Chemistry ; Carbon nanofiber ; Nanofiber ; Nanotechnology ; Carbon fibers ; Electrospinning ; Chemical engineering ; Surface modification ; Carbon nanotube ; Organic chemistry ; Polymer ; Composite material ; Catalysis ; Physical chemistry Electrospun Nanofibers in Biomedical Applications ; Supercapacitor Materials and Fabrication ; Advanced Sensor and Energy Harvesting Materials
Authors: Valérie Forest
Source: Nanomaterials (review, 2022-04-14)
Field: Materials Science — Materials Chemistry

Experimental and Computational Nanotoxicology—Complementary Approaches for Nanomaterial Hazard Assessment The growing development and applications of nanomaterials lead to an increasing release of these materials in the environment. The adverse effects they may elicit on ecosystems or human health are not always fully characterized. Such potential toxicity must be carefully assessed with the underlying mechanisms elucidated. To that purpose, different approaches can be used. First, experimental toxicology consisting of conducting in vitro or in vivo experiments (including clinical studies) can be used to evaluate the nanomaterial hazard. It can rely on variable models (more or less complex), allowing the investigation of different biological endpoints. The respective advantages and limitations of in vitro and in vivo models are discussed as well as some issues associated with experimental nanotoxicology. Perspectives of future developments in the field are also proposed. Second, computational nanotoxicology, i.e., in silico approaches, can be used to predict nanomaterial toxicity. In this context, we describe the general principles, advantages, and limitations especially of quantitative structure-activity relationship (QSAR) models and grouping/read-across approaches. The aim of this review is to provide an overview of these different approaches based on examples and highlight their complementarity. Nanotoxicology ; Context (archaeology) ; Hazard analysis ; Hazard ; Biochemical engineering ; Computer science ; Human health ; Risk analysis (engineering) ; In silico ; Nanotechnology ; Computational biology ; Engineering ; Biology ; Materials science ; Medicine ; Reliability engineering ; Ecology ; Nanoparticle Nanoparticles: synthesis and applications ; Computational Drug Discovery Methods ; Microplastics and Plastic Pollution
Authors: Aleksandar Ćirić | Ł. Marciniak | Miroslav D. Dramićanin
Source: Scientific Reports (article, 2022-01-12)
Field: Materials Science — Materials Chemistry

Self-referenced method for the Judd–Ofelt parametrisation of the Eu3+ excitation spectrum Judd-Ofelt theory is a cornerstone of lanthanides' spectroscopy given that it describes 4f<sup>n</sup> emissions and absorptions of lanthanide ions using only three intensity parameters. A self-referenced technique for computing Judd-Ofelt intensity parameters from the excitation spectra of Eu<sup>3+</sup>-activated luminescent materials is presented in this study along with an explanation of the parametrisation procedure and free user-friendly web application. It uses the integrated intensities of the <sup>7</sup>F<sub>0</sub> → <sup>5</sup>D<sub>2</sub>, <sup>7</sup>F<sub>0</sub> → <sup>5</sup>D<sub>4</sub>, and <sup>7</sup>F<sub>0</sub> → <sup>5</sup>L<sub>6</sub> transitions in the excitation spectrum for estimation and the integrated intensity of the <sup>7</sup>F<sub>0</sub> → <sup>5</sup>D<sub>1</sub> magnetic dipole transition for calibration. This approach facilitates an effortless derivation of the Ω<sub>6</sub> intensity parameter, which is challenging to compute precisely by Krupke's parametrisation of the emission spectrum and, therefore, often omitted in published research papers. Compared to the parametrisation of absorption spectra, the described method is more accurate, can be applied to any material form, and requires a single excitation spectrum. Excitation ; Absorption spectroscopy ; Lanthanide ; Ion ; Luminescence ; Intensity (physics) ; Atomic physics ; Emission spectrum ; Spectral line ; Physics ; Spectrum (functional analysis) ; Spectroscopy ; Absorption (acoustics) ; Chemistry ; Computational physics ; Optics ; Quantum mechanics Lanthanide and Transition Metal Complexes ; Luminescence Properties of Advanced Materials ; Radioactive element chemistry and processing