jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Vladimir A. Larionov | Ben L. Feringa | Yuri N. Belokoń
Source: Chemical Society Reviews (review, 2021-01-01)
Field: Chemistry — Organic Chemistry

Enantioselective “organocatalysis in disguise” by the ligand sphere of chiral metal-templated complexes Asymmetric catalysis holds a prominent position among the important developments in chemistry during the 20th century. This was acknowledged by the 2001 Nobel Prize in chemistry awarded to Knowles, Noyori, and Sharpless for their development of chiral metal catalysts for organic transformations. The key feature of the catalysts was the crucial role of the chiral ligand and the nature of the metal ions, which promoted the catalytic conversions of the substrates via direct coordination. Subsequently the development of asymmetric organic catalysis opened new avenues to the synthesis of enantiopure compounds, avoiding any use of metal ions. Recently, an alternative approach to asymmetric catalysis emerged that relied on the catalytic functions of the ligands themselves boosted by coordination to metal ions. In other words, in these hybrid chiral catalysts the substrates are activated not by the metal ions but by the ligands. The activation and enantioselective control occurred via well-orchestrated and custom-tailored non-covalent interactions of the substrates with the ligand sphere of chiral metal complexes. In these metal-templated catalysts, the metal served either as a template (a purely structural role), or it constituted the exclusive source of chirality (metal-centred chirality due to the spatial arrangement of achiral or chiral bi-/tridentate ligands around an octahedral metal centre), and/or it increased the Brønsted acidity of the ligands. Although the field is still in its infancy, it represents an inspiring combination of both metal and organic catalysis and holds major unexplored potential to push the frontiers of asymmetric catalysis. Here we present an overview of this emerging field discussing the principles, applications and perspectives on the catalytic use of chiral metal complexes that operate as "organocatalysts in disguise". It has been demonstrated that these chiral metal complexes are efficient and provide high stereoselective control in asymmetric hydrogen bonding catalysis, phase-transfer catalysis, Brønsted acid/base catalysis, enamine catalysis, nucleophilic catalysis, and photocatalysis as well as bifunctional catalysis. Also, many of the catalysts have been identified as highly effective catalysts at remarkably low catalyst loadings. These hybrid systems offer many opportunities in the synthesis of chiral compounds and represent promising alternatives to metal-based and organocatalytic asymmetric transformations. Enantioselective synthesis ; Organocatalysis ; Catalysis ; Chiral ligand ; Ligand (biochemistry) ; Chemistry ; Metal ; Combinatorial chemistry ; Metal-organic framework ; Stereochemistry ; Organic chemistry ; Biochemistry Asymmetric Synthesis and Catalysis ; Asymmetric Hydrogenation and Catalysis ; Axial and Atropisomeric Chirality Synthesis
Authors: Tianying Sun | Yaobin Gao | Yangyang Du | Lei Zhou | Xian Chen
Source: Frontiers in Chemistry (review, 2021-01-25)
Field: Chemistry — Inorganic Chemistry

Recent Advances in Developing Lanthanide Metal–Organic Frameworks for Ratiometric Fluorescent Sensing Fluorescent probes have attracted special attention in developing optical sensor systems due to their reliable and rapid fluorescent response upon reaction with the analyte. Comparing to traditional fluorescent sensing systems that employ the intensity of only a single emission, ratiometric fluorescent sensors exhibit higher sensitivity and allow fast visual screening of analytes because of quantitatively analyzing analytes through the emission intensity ratio at two or more wavelengths. Lanthanide metal-organic frameworks (LnMOFs) are highly designable multifunctional luminescent materials as lanthanide ions, organic ligands, and guest metal ions or chromophores are all potential sources for luminescence. They thus have been widely employed as ratiometric fluorescent sensors. This mini review summarized the basic concept, optical features, construction strategies, and the ratiometric fluorescent sensing mechanisms of dual-emitting LnMOFs. The review ends with a discussion on the prospects, challenges, and new direction in designing LnMOF-based ratiometric fluorescent sensors. Fluorescence ; Lanthanide ; Analyte ; Chromophore ; Luminescence ; Metal ions in aqueous solution ; Materials science ; Nanotechnology ; Optical sensing ; Metal-organic framework ; Photochemistry ; Metal ; Chemistry ; Ion ; Optoelectronics ; Organic chemistry ; Optics ; Physical chemistry Metal-Organic Frameworks: Synthesis and Applications ; Molecular Sensors and Ion Detection ; Advanced Nanomaterials in Catalysis
Authors: Chiun‐Wei Huang | Chia-Pao Chuang | Yanjun Chen | Hsu-Yuan Wang | Jiajia Lin | Chiung-Yin Huang | Kuo‐Chen Wei | Feng‐Ting Huang
Source: Journal of Nanobiotechnology (article, 2021-06-13)
Field: Materials Science — Biomaterials

Integrin α2β1-targeting ferritin nanocarrier traverses the blood–brain barrier for effective glioma chemotherapy We believe that this versatile nanoparticle has established a proof-of-concept platform to enable more accurate brain tumor targeting and precision treatment arrangements. Additionally, this unique RMT based ferritin drug delivery technique would accelerate the clinical development of an innovative drug delivery strategy for central nervous system diseases with limited side effects in translational medicine. Transcytosis ; Nanocarriers ; Glioma ; Transferrin receptor ; Blood–brain barrier ; Drug delivery ; Transferrin ; Ferritin ; In vivo ; Cancer research ; Targeted drug delivery ; Integrin ; Paclitaxel ; Chemistry ; Endocytosis ; Pharmacology ; Drug ; Medicine ; Receptor ; Biology ; Chemotherapy ; Central nervous system ; Internal medicine ; Biochemistry Nanoparticle-Based Drug Delivery ; Iron Metabolism and Disorders ; Drug Transport and Resistance Mechanisms
Authors: Lord Ugwu | Yasser Morgan | Hussameldin Ibrahim
Source: International Journal of Hydrogen Energy (article, 2021-11-24)
Field: Materials Science — Materials Chemistry

Application of density functional theory and machine learning in heterogenous-based catalytic reactions for hydrogen production Steam reforming ; Hydrogen production ; Hydrogen ; Catalysis ; Hydrogen economy ; Carbon dioxide reforming ; Chemistry ; Methane reformer ; Methane ; Process engineering ; Density functional theory ; Biochemical engineering ; Computer science ; Syngas ; Computational chemistry ; Organic chemistry Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Electrocatalysts for Energy Conversion
Authors: Shinya Mine | Motoshi Takao | Taichi Yamaguchi | Takashi Toyao | Zen Maeno | S. M. A. Hakim Siddiki | Satoru Takakusagi | Ken‐ichi Shimizu | Ichigaku Takigawa
Source: ChemCatChem (article, 2021-05-31)
Field: Materials Science — Materials Chemistry

Analysis of Updated Literature Data up to 2019 on the Oxidative Coupling of Methane Using an Extrapolative Machine‐Learning Method to Identify Novel Catalysts Abstract We have constructed and analyzed an updated dataset consisting of 4759 experimental datapoints for the oxidative coupling of methane (OCM) reaction based on literature data reported before 2020 (∼2019) using machine learning (ML) methods. Several ML methods, including random forest regression (RFR), extra trees regression (ETR), and gradient boosting regression with XGBoost (XGB), were used in conjunction with our proposed approach, in which elemental features are used as input representations rather than inputting the catalyst compositions directly. A recent research trend, namely, the extensive exploration of Mn/Na 2 WO 4 /SiO 2 catalyst systems in recent years due to their high activity and durability, was clearly reflected in the dataset analysis. An ML model for the prediction of the reaction outcome (C 2 yield) was successfully developed, and feature importance scores and SHapley Additive exPlanations (SHAP) values were calculated based on ETR and XGB, respectively, to identify the input variables with the greatest influence on the catalyst performance and observe how these important variables affect the C 2 yield in the OCM. The discovery and optimization of catalytic processes using ML as a “surrogate” model were explored, and promising catalytic system candidates for the OCM reaction were identified. Notably, the developed ML model predicted catalysts containing elements that do not appear in the OCM dataset. This clearly demonstrates desirably high potential of our ML model to enable extrapolative predictions for ML‐aided future catalysis research. Catalysis ; Yield (engineering) ; Oxidative coupling of methane ; Random forest ; Feature selection ; Regression analysis ; Regression ; Gradient boosting ; Chemistry ; Computer science ; Machine learning ; Artificial intelligence ; Biological system ; Mathematics ; Statistics ; Thermodynamics ; Organic chemistry ; Physics Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Catalytic Processes in Materials Science
Authors: Sohyun Park | Sunhyeon Park | Young Park | Muhammad Hilmy Alfaruqi | Jang‐Yeon Hwang | Jaekook Kim
Source: Energy & Environmental Science (article, 2021-01-01)
Field: Materials Science — Materials Chemistry

A new material discovery platform of stable layered oxide cathodes for K-ion batteries A new materials discovery platform based on combined machine learning (ML) and density functional theory (DFT) for screening and experimental validation is proposed for designing a stable K x MnO 2 cathode in K-ion batteries. Cathode ; Density functional theory ; Ion ; Materials science ; Nanotechnology ; Oxide ; Computer science ; Chemistry ; Computational chemistry ; Physical chemistry ; Metallurgy ; Organic chemistry Machine Learning in Materials Science ; Advancements in Battery Materials ; X-ray Diffraction in Crystallography
Authors: Sheng Zhang | Leyun Wang | Gaoming Zhu | Martin Diehl | Alireza Maldar | Xiaoqing Shang | Xiaoqin Zeng
Source: International Journal of Plasticity (article, 2021-12-17)
Field: Materials Science — Materials Chemistry

Predicting grain boundary damage by machine learning Grain boundary ; Materials science ; Nucleation ; Microstructure ; Texture (cosmology) ; Deformation (meteorology) ; Artificial intelligence ; Stress (linguistics) ; Machine learning ; Ultimate tensile strength ; Plasticity ; Grain boundary strengthening ; Metallurgy ; Composite material ; Computer science ; Thermodynamics Microstructure and mechanical properties ; Metal and Thin Film Mechanics ; Magnesium Alloys: Properties and Applications
Authors: Ahmer A. B. Baloch | Saad M. Alqahtani | Faisal Mumtaz | Ali H. Muqaibel | Sergey N. Rashkeev | Fahhad H. Alharbi
Source: Physical Review Materials (article, 2021-04-15)
Field: Materials Science — Materials Chemistry

Extending Shannon's ionic radii database using machine learning The authors extend the ionic radii database of Shannon's seminal work using machine learning regression. The developed consolidated table will allow prediction of material properties with high accuracy by considering the definite ionic radius value based on the oxidation state and coordination number. The work is relevant to the evolving material informatics field and has applications in many related fields. Ionic radius ; Field (mathematics) ; RADIUS ; Ionic bonding ; Materials informatics ; Table (database) Machine Learning in Materials Science ; Inorganic Chemistry and Materials ; X-ray Diffraction in Crystallography
Authors: Zifeng Wang | Shizhuo Ye | Hao Wang | Jin He | Qijun Huang | Sheng Chang
Source: npj Computational Materials (article, 2021-01-25)
Field: Materials Science — Materials Chemistry

Machine learning method for tight-binding Hamiltonian parameterization from ab-initio band structure Abstract The tight-binding (TB) method is an ideal candidate for determining electronic and transport properties for a large-scale system. It describes the system as real-space Hamiltonian matrices expressed on a manageable number of parameters, leading to substantially lower computational costs than the ab-initio methods. Since the whole system is defined by the parameterization scheme, the choice of the TB parameters decides the reliability of the TB calculations. The typical empirical TB method uses the TB parameters directly from the existing parameter sets, which hardly reproduces the desired electronic structures quantitatively without specific optimizations. It is thus not suitable for quantitative studies like the transport property calculations. The ab-initio TB method derives the TB parameters from the ab-initio results through the transformation of basis functions, which achieves much higher numerical accuracy. However, it assumes prior knowledge of the basis and may encompass truncation error. Here, a machine learning method for TB Hamiltonian parameterization is proposed, within which a neural network (NN) is introduced with its neurons acting as the TB matrix elements. This method can construct the empirical TB model that reproduces the given ab-initio energy bands with predefined accuracy, which provides a fast and convenient way for TB model construction and gives insights into machine learning applications in physical problems. Ab initio ; Hamiltonian (control theory) ; Computer science ; Tight binding ; Ab initio quantum chemistry methods ; Statistical physics ; Artificial neural network ; Algorithm ; Electronic structure ; Applied mathematics ; Artificial intelligence ; Mathematics ; Mathematical optimization ; Physics ; Quantum mechanics Machine Learning in Materials Science ; Ferroelectric and Negative Capacitance Devices ; Fuel Cells and Related Materials
Authors: Majedeh Gheytanzadeh | Alireza Baghban | Sajjad Habibzadeh | Amin Esmaeili | Otman Abida | Ahmad Mohaddespour | Muhammad Tajammal Munir
Source: Scientific Reports (article, 2021-08-03)
Field: Chemistry — Inorganic Chemistry

Towards estimation of CO2 adsorption on highly porous MOF-based adsorbents using gaussian process regression approach In recent years, new developments in controlling greenhouse gas emissions have been implemented to address the global climate conservation concern. Indeed, the earth's average temperature is being increased mainly due to burning fossil fuels, explicitly releasing high amounts of CO<sub>2</sub> into the atmosphere. Therefore, effective capture techniques are needed to reduce the concentration of CO<sub>2</sub>. In this regard, metal organic frameworks (MOFs) have been known as the promising materials for CO<sub>2</sub> adsorption. Hence, study on the impact of the adsorption conditions along with the MOFs structural properties on their ability in the CO<sub>2</sub> adsorption will open new doors for their further application in CO<sub>2</sub> separation technologies as well. However, the high cost of the corresponding experimental study together with the instrument's error, render the use of computational methods quite beneficial. Therefore, the present study proposes a Gaussian process regression model with four kernel functions to estimate the CO<sub>2</sub> adsorption in terms of pressure, temperature, pore volume, and surface area of MOFs. In doing so, 506 CO<sub>2</sub> uptake values in the literature have been collected and assessed. The proposed GPR models performed very well in which the exponential kernel function, was shown as the best predictive tool with R<sup>2</sup> value of 1. Also, the sensitivity analysis was employed to investigate the effectiveness of input variables on the CO<sub>2</sub> adsorption, through which it was determined that pressure is the most determining parameter. As the main result, the accurate estimate of CO<sub>2</sub> adsorption by different MOFs is obtained by briefly employing the artificial intelligence concept tools. Adsorption ; Porosity ; Computer science ; Materials science ; Gaussian ; Fossil fuel ; Process (computing) ; Greenhouse gas ; Porous medium ; Biological system ; Process engineering ; Environmental science ; Chemistry ; Geology ; Organic chemistry ; Composite material Metal-Organic Frameworks: Synthesis and Applications ; Carbon Dioxide Capture Technologies ; Membrane Separation and Gas Transport
Authors: Huỳnh Linh Đinh | Nam Trung Tran | Dai Xuan Trinh
Source: Journal of Analytical Methods in Chemistry (article, 2021-06-12)
Field: Chemistry — Inorganic Chemistry

Investigation into the Adsorption of Methylene Blue and Methyl Orange by UiO-66-NO2 Nanoparticles In this work, the adsorptive removal of methylene blue and methyl orange by UiO-66-NO<sub>2</sub> nanoparticles was studied. The influence of pH on the adsorption capacity was assessed. The kinetics of the adsorption process were investigated and compared with pseudo-first-order, pseudo-second-order, Elovich, and intraparticle models. The kinetics of the adsorption fits moderately with the pseudo-first-order, but perfectly fits with pseudo-second-order models, and has a very good fit with the Elovich and intraparticle models. The adsorption isotherms were measured and compared with the Langmuir and Freundlich models. The adsorption capacity of methyl orange (MO) on UiO-66-NO<sub>2</sub> nanoparticles (142.9 mg/g) was over three times higher than that of methylene blue (MB) on the nanoparticles (41.7 mg/g). The discrepancy between these capacities was attributed to the presence of the -NO<sub>2</sub> functional group, which caused a strong negative mesomeric effect in the metal-organic framework structure. Adsorption ; Methylene blue ; Freundlich equation ; Methyl orange ; Chemistry ; Nanoparticle ; Langmuir ; Kinetics ; Langmuir adsorption model ; Nuclear chemistry ; Inorganic chemistry ; Chemical engineering ; Organic chemistry ; Catalysis Metal-Organic Frameworks: Synthesis and Applications ; Adsorption and biosorption for pollutant removal ; Nanomaterials for catalytic reactions
Authors: Badriyah S. Alotaibi | Manal Buabeid | Nihal Abdalla Ibrahim | Zelal Kharaba | Munazza Ijaz | Sobia Noreen | Ghulam Murtaza
Source: International Journal of Nanomedicine (review, 2021-11-01)
Field: Materials Science — Biomaterials

Potential of Nanocarrier-Based Drug Delivery Systems for Brain Targeting: A Current Review of Literature Abstract: The advent of nanotechnologies such as nanocarriers and nanotherapeutics has changed the treatment strategy and developed a more efficacious novel drug delivery system. Various drug delivery systems are focused on drug-targeting of brain cells. However, the manifestation of the brain barrier is the main hurdle for the effective delivery of chemotherapeutics, ultimately causing treatment failure of various drugs. To solve this problem, various nanocarrier-based drug delivery system has been developed for brain targeting. This review outlines nanocarrier-based composites for different brain diseases and highlights nanocarriers for drug targeting towards brain cells. It also summarizes the latest developments in nanocarrier-based delivery systems containing liposomal systems, dendrimers, polymeric micelles, polymeric nanocarriers, quantum dots (QDs), and gold nanoparticles. Besides, the optimal properties of nanocarriers and therapeutic implications for brain targeting have been extensively studied. Finally, the potential applications and research opportunities for nanocarriers in brain targeting are discussed. Keywords: brain targeting, nanocarriers, blood–brain barrier, nanotherapeutics, in-vivo Corrigendum for this paper has been published Nanocarriers ; Drug delivery ; Drug ; Nanotechnology ; Targeted drug delivery ; Nanomedicine ; Liposome ; Drug carrier ; Materials science ; Pharmacology ; Medicine ; Nanoparticle Nanoparticle-Based Drug Delivery ; Graphene and Nanomaterials Applications ; Advanced Drug Delivery Systems
Authors: Dominic Bara | Emily G. Meekel | Ignas Pakamorė | Claire Wilson | Sanliang Ling | Ross S. Forgan
Source: Materials Horizons (article, 2021-01-01)
Field: Chemistry — Inorganic Chemistry

Exploring and expanding the Fe-terephthalate metal–organic framework phase space by coordination and oxidation modulation The synthesis of phase pure metal-organic frameworks (MOFs) - network solids of metal clusters connected by organic linkers - is often complicated by the possibility of forming multiple diverse phases from one metal-ligand combination. For example, there are at least six Fe-terephthalate MOFs reported to date, with many examples in the literature of erroneous assignment of phase based on diffraction data alone. Herein, we show that modulated self-assembly can be used to influence the kinetics of self-assembly of Fe-terephthalate MOFs. We comprehensively assess the effect of addition of both coordinating modulators and pH modulators on the outcome of syntheses, as well as probing the influence of the oxidation state of the Fe precursor (oxidation modulation) and the role of the counteranion on the phase(s) formed. In doing so, we shed light on the thermodynamic landscape of this phase system, uncover mechanistics of modulation, provide robust routes to phase pure materials, often as single crystals, and introduce two new Fe-terephthalate MOFs to an already complex system. The results highlight the potential of modulated self-assembly to bring precision control and new structural diversity to systems that have already received significant study. Phase (matter) ; Materials science ; Metal ; Modulation (music) ; Metal-organic framework ; Coordination complex ; Kinetic control ; Nanotechnology ; Kinetic energy ; Chemical engineering ; Metallurgy ; Chemistry ; Physical chemistry ; Catalysis ; Organic chemistry ; Physics ; Adsorption ; Engineering Metal-Organic Frameworks: Synthesis and Applications ; Covalent Organic Framework Applications ; Dendrimers and Hyperbranched Polymers
Authors: Mariam Abdulaziz M. Tarkistani | Varsha Komalla | Veysel Kayser
Source: Nanomaterials (review, 2021-05-06)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Recent Advances in the Use of Iron–Gold Hybrid Nanoparticles for Biomedical Applications Recently, there has been an increased interest in iron-gold-based hybrid nanostructures, due to their combined outstanding optical and magnetic properties resulting from the usage of two separate metals. The synthesis of these nanoparticles involves thermal decomposition and modification of their surfaces using a variety of different methods, which are discussed in this review. In addition, different forms such as core-shell, dumbbell, flower, octahedral, star, rod, and Janus-shaped hybrids are discussed, and their unique properties are highlighted. Studies on combining optical response in the near-infrared window and magnetic properties of iron-gold-based hybrid nanoparticles as multifunctional nanoprobes for drug delivery, magnetic-photothermal heating as well as contrast agents during magnetic and optical imaging and magnetically-assisted optical biosensing to detect traces of targeted analytes inside the body has been reviewed. Materials science ; Nanotechnology ; Photothermal therapy ; Nanoparticle ; Colloidal gold ; Magnetic nanoparticles ; Biosensor ; Nanostructure ; Surface modification ; Janus ; Drug delivery ; Chemical engineering Gold and Silver Nanoparticles Synthesis and Applications ; Laser-Ablation Synthesis of Nanoparticles ; Nanoparticle-Based Drug Delivery