jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Ying Zhang | Torin C. Peck | Gunugunuri K. Reddy | Debasish Banerjee | Hongfei Jia | Charles A. Roberts | Chen Ling
Source: ACS Catalysis (article, 2022-08-12)
Field: Materials Science — Materials Chemistry

Descriptor-Free Design of Multicomponent Catalysts Multicomponent alloys and oxides are material systems, offering the great promise of unique yet advantageous catalytic properties through appropriate choice of compositions. However, strategic design of the highly active multicomponent catalyst is challenged by the vast number of potential candidates and complex inter-component effects. Herein, we demonstrate the successful employment of Bayesian optimization (BO) to improve the experimental measured activity as a direct function of compositional variables without educating physical knowledge to the machine. We applied BO in screening spinel CraMnbFecCodNieCufZn3–a–b–c–d–e–fO4 for the decomposition of nitric oxide into environmentally friendly nitrogen. Starting with 30 manually surveyed samples, 35 more samples were measured, and six oxides were discovered with higher specific activities. The best candidate discovered in the current work, Co2.1Cu0.6Zn0.2Mn0.1O4, showed significantly better catalytic performance than the benchmark standard. Although not directly educated about the underlying physical origin of variation in specific activity, the optimization balanced the exploration to locate promising regions and exploitation to survey in the identified region of Mn-containing subspace. The success to directly optimize the experimental measured specific activity in the large compositional space through a small sampled data set demonstrates the great potential of BO in the discovery and design of multicomponent catalysts. Catalysis ; Bayesian optimization ; Spinel ; Decomposition ; Benchmark (surveying) ; Subspace topology ; Computer science ; Chemical space ; Function (biology) ; Material Design ; Design of experiments ; Biochemical engineering ; Environmentally friendly ; Biological system ; Materials science ; Chemistry ; Machine learning ; Artificial intelligence ; Mathematics ; Drug discovery ; Organic chemistry ; Engineering ; Statistics Machine Learning in Materials Science ; Catalytic Processes in Materials Science ; Electrocatalysts for Energy Conversion
Authors: Vinh X. Truong | Katharina Ehrmann | Maximilian Seifermann | Pavel A. Levkin | Christopher Barner‐Kowollik
Source: Chemistry - A European Journal (article, 2022-02-25)
Field: Materials Science — Materials Chemistry

Wavelength Orthogonal Photodynamic Networks The ability of light to remotely control the properties of soft matter materials in a dynamic fashion has fascinated material scientists and photochemists for decades. However, only recently has our ability to map photochemical reactivity in a finely wavelength resolved fashion allowed for different colors of light to independently control the material properties of polymer networks with high precision, driven by monochromatic irradiation enabling orthogonal reaction control. The current concept article highlights the progress in visible light-induced photochemistry and explores how it has enabled the design of polymer networks with dynamically adjustable properties. We will explore current applications ranging from dynamic hydrogel design to the light-driven adaptation of 3D printed structures on the macro- and micro-scale. While the alternation of mechanical properties via remote control is largely reality for soft matter materials, we herein propose the next frontiers for adaptive properties, including remote switching between conductive and non-conductive properties, hydrophobic and hydrophilic surfaces, fluorescent or non-fluorescent, and cell adhesive vs. cell repellent properties. Materials science ; Fluorescence ; Monochromatic color ; Nanotechnology ; Optoelectronics ; Wavelength ; Adhesive ; Soft matter ; Optics ; Chemical engineering ; Physics ; Layer (electronics) Photochromic and Fluorescence Chemistry ; Photoreceptor and optogenetics research ; Supramolecular Self-Assembly in Materials
Authors: Terek Li | Tianhao Chen | Xuechen Shen | Haotian Shi | Elahe Jabari | Hani E. Naguib
Source: Nanoscale Advances (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

A binder jet 3D printed MXene composite for strain sensing and energy storage application Polymer composite materials have been proven to have numerous electrical related applications ranging from energy storage to sensing, and 3D printing is a promising technique to fabricate such materials with a high degree of freedom and low lead up time. Compared to the existing 3D printing technique for polymer materials, binder jet (BJ) printing offers unique advantages such as a fast production rate, room temperature printing of large volume objects, and the ability to print complex geometries without additional support materials. However, there is a serious lack of research in BJ printing of polymer materials. In this work we introduce a strategy to print poly(vinyl alcohol) composites with MXene-surfactant ink. By ejecting highly conductive MXene particles onto a PVOH matrix, the resulting sample achieved conductive behaviour in the order of mS m<sup>-1</sup> with demonstrated potential for strain sensing and energy storage. This work demonstrates that BJ printing has the potential to directly fabricate polymer composite materials with different end applications. Materials science ; Composite number ; 3d printed ; Inkwell ; Composite material ; Capacitor ; 3D printing ; Jet (fluid) ; Strain (injury) ; Energy storage ; Nanoparticle ; Nanotechnology ; Electrical engineering ; Engineering ; Manufacturing engineering ; Aerospace engineering ; Voltage MXene and MAX Phase Materials ; Advanced Sensor and Energy Harvesting Materials ; Advanced Memory and Neural Computing
Authors: Huangjun Deng | Zhan Yang | Gaoyu Li | Dongyu Ma | Zongliang Xie | Wenlang Li | Zhu Mao | Juan Zhao | Zhiyong Yang | Yi Zhang | Zhenguo Chi
Source: Chemical Engineering Journal (article, 2022-02-28)
Field: Materials Science — Materials Chemistry

Dynamic organic mechanoluminescence (ML): The roles of Mechano-induced conformational isomer and energy transfer from ML to photoluminescence (PL) Mechanoluminescence ; Photoluminescence ; Amorphous solid ; Materials science ; Grinding ; Crystal (programming language) ; Energy transfer ; Chemical engineering ; Photochemistry ; Chemistry ; Optoelectronics ; Crystallography ; Chemical physics ; Luminescence ; Composite material ; Computer science Luminescence and Fluorescent Materials ; Organic Light-Emitting Diodes Research ; Polydiacetylene-based materials and applications
Authors: Yang Yang | Xu Jing | Jing Zhang | Fengyu Yang | Chunying Duan
Source: Nature Communications (article, 2022-04-11)
Field: Chemistry — Organic Chemistry

Modifying electron injection kinetics for selective photoreduction of nitroarenes into cyclic and asymmetric azo compounds Modifying the reactivity of substrates by encapsulation is essential for microenvironment catalysts. Herein, we report an alternative strategy that modifies the entry behaviour of reactants into the microenvironment and substrate inclusion thermodynamics related to the capsule to control the electron injection kinetics and the selectivity of products from the nitroarenes photoreduction. The strategy includes the orchestration of capsule openings to control the electron injection kinetics of electron donors, and the capsule's pocket to encapsulate more than one nitroarene molecules, facilitating a condensation reaction between the in situ formed azanol and nitroso species to produce azo product. The conceptual microenvironment catalyst endows selective conversion of asymmetric azo products from different nitroarenes, wherein, the estimated diameter and inclusion Gibbs free energy of substrates are used to control and predict the selectivity of products. Inhibition experiments confirm a typical enzymatic conversion, paving a new avenue for rational design of photocatalysts toward green chemistry. Kinetics ; Selectivity ; Chemistry ; Catalysis ; Substrate (aquarium) ; Reactivity (psychology) ; Molecule ; Chemical engineering ; Combinatorial chemistry ; Organic chemistry Nanomaterials for catalytic reactions ; Advanced Photocatalysis Techniques ; Electrocatalysts for Energy Conversion
Authors: M.A. del Valle | Marta Ximenis | Xabier Lopez de Pariza | Julian M. W. Chan | Haritz Sardón
Source: Angewandte Chemie International Edition (review, 2022-06-14)
Field: Chemistry — Organic Chemistry

Spotting Trends in Organocatalyzed and Other Organomediated (De)polymerizations and Polymer Functionalizations Organocatalysis has evolved into an effective complement to metal- or enzyme-based catalysis in polymerization, polymer functionalization, and depolymerization. The ease of removal and greater sustainability of organocatalysts relative to transition-metal-based ones has spurred development in specialty applications, e.g., medical devices, drug delivery, optoelectronics. Despite this, the use of organocatalysis and other organomediated reactions in polymer chemistry is still rapidly developing, and we envisage their rapidly growing application in nascent areas such as controlled radical polymerization, additive manufacturing, and chemical recycling in the coming years. In this Review, we describe ten trending areas where we anticipate paradigm shifts resulting from novel organocatalysts and other transition-metal-free conditions. We highlight opportunities and challenges and detail how new discoveries could lead to previously inaccessible functional materials and a potentially circular plastics economy. Organocatalysis ; Polymerization ; Nanotechnology ; Depolymerization ; Chemistry ; Polymer ; Polymer science ; Materials science ; Catalysis ; Organic chemistry ; Enantioselective synthesis Advanced Polymer Synthesis and Characterization ; biodegradable polymer synthesis and properties ; Organometallic Complex Synthesis and Catalysis
Authors: Simona Premcheska | Mirijam Lederer | Anna M. Kaczmarek
Source: Chemical Communications (review, 2022-01-01)
Field: Materials Science — Materials Chemistry

The importance, status, and perspectives of hybrid lanthanide-doped upconversion nanothermometers for theranostics Theranostics combines diagnostics and therapy in a single multifunctional system. Multifunctional upconversion luminescent lanthanide-doped nanothermometers for theranostic purposes offer non-invasive and sensitive multimodal performance in the biomedical field over traditional temperature measurement methods. Despite existing challenges, various studies on hybrid upconversion nanothermometers show substantial progress for (bio)imaging, temperature sensing, photodynamic and photothermal therapy, as well as drug delivery applications. The beauty of such an approach is that it unfolds possibilities to combine diagnostics and therapy in a single particle, which can modify the way certain diseases are treated, hence change the entire healthcare scene. Photon upconversion ; Photothermal therapy ; Lanthanide ; Nanotechnology ; Materials science ; Photodynamic therapy ; Drug delivery ; Doping ; Cancer therapy ; Computer science ; Optoelectronics ; Cancer ; Medicine ; Chemistry Luminescence Properties of Advanced Materials ; Gas Sensing Nanomaterials and Sensors ; Transition Metal Oxide Nanomaterials
Authors: F. Javier de la Mata | Rafael Gómez | Jesús Cano | Javier Sánchez‐Nieves | Paula Ortega | Sandra García‐Gallego
Source: Wiley Interdisciplinary Reviews Nanomedicine and Nanobiotechnology (review, 2022-11-23)
Field: Materials Science — Polymers and Plastics

Carbosilane dendritic nanostructures, highly versatile platforms for pharmaceutical applications Dendrimers are multifunctional molecules with well-defined size and structure due to the step-by-step synthetic procedures required in their preparation. Dendritic constructs based on carbosilane scaffolds present carbon-carbon and carbon-silicon bonds, which results in stable, lipophilic, inert, and flexible structures. These properties are highly appreciated in different areas, including the pharmaceutical field, as they can increase the interaction with cell membranes and improve the therapeutic action. This article summarizes the most recent advances in the pharmaceutical applications of carbosilane dendritic molecules, from therapeutics to diagnostics and prevention tools. Dendrimers decorated with cationic, anionic, or other moieties, including metallodendrimers; supramolecular assemblies; dendronized nanoparticles and surfaces; as well as dendritic networks like hydrogels are described. The collected examples confirm the potential of carbosilane dendrimers and dendritic materials as antiviral or antibacterial agents; in therapy against cancer, neurodegenerative disease, or oxidative stress; or many other biomedical applications. This article is categorized under: Nanotechnology Approaches to Biology > Nanoscale Systems in Biology Therapeutic Approaches and Drug Discovery > Nanomedicine for Infectious Disease Therapeutic Approaches and Drug Discovery > Nanomedicine for Oncologic Disease. Nanomedicine ; Dendrimer ; Nanotechnology ; Drug delivery ; Supramolecular chemistry ; Materials science ; Nanoparticle ; Chemistry ; Molecule ; Organic chemistry Dendrimers and Hyperbranched Polymers ; Chemical Synthesis and Analysis ; Click Chemistry and Applications
Authors: Guanghao Huang | Régis Guillot | Cyrille Kouklovsky | Boris Maryasin | Aurélien de la Torre
Source: Angewandte Chemie International Edition (article, 2022-08-30)
Field: Chemistry — Organic Chemistry

Diastereo‐ and Enantioselective Inverse‐Electron‐Demand Diels–Alder Cycloaddition between 2‐Pyrones and Acyclic Enol Ethers A broadly applicable diastereo- and enantioselective inverse-electron-demand Diels-Alder reaction of 2-pyrones and acyclic enol ethers is reported herein. Using a copper(II)-BOX catalytic system, bridged bicyclic lactones are obtained in very high yields (up to 99 % yield) and enantioselectivities (up to 99 % ee) from diversely substituted 2-pyrones and acyclic enol ethers. Mechanistic experiments as well as DFT calculations indicate the occurrence of a stepwise mechanism. The synthetic potential of the bridged bicyclic lactones is showcased by the enantioselective synthesis of polyfunctional cyclohexenes and cyclohexadienes, as well as a carbasugar unit. Enantioselective synthesis ; Enol ; Bicyclic molecule ; Cycloaddition ; Chemistry ; Yield (engineering) ; Catalysis ; Organic chemistry ; Stereochemistry ; Materials science Asymmetric Synthesis and Catalysis ; Synthetic Organic Chemistry Methods ; Chemical synthesis and alkaloids
Authors: Zhiyang Wang | John M. Andjaba | Christopher J. Rybak | Liyan You | Christopher Uyeda | Jianguo Mei
Source: Chemical Engineering Journal (article, 2022-12-19)
Field: Materials Science — Polymers and Plastics

Black-to-Transmissive dual polymer complementary electrochromics with high coloration efficiency Dual (grammatical number) ; Polymer ; Materials science ; Chemistry ; Chemical engineering ; Composite material ; Engineering ; Art Conducting polymers and applications ; Transition Metal Oxide Nanomaterials ; Polydiacetylene-based materials and applications
Authors: Supreeta Sen | Jayabrata Das | Debabrata Maiti
Source: Tetrahedron Chem (article, 2022-01-11)
Field: Chemistry — Organic Chemistry

Strategies to transform remote C(sp3)-H bonds of amino acid derivatives Amino acids and peptides play an important role in nature as well as in the pharmaceutical industry. Therefore, transformation of amino acids and peptides into their unnatural derivatives via step and atom economical ways is highly demanding. Although, a number of methods exist for selective reactions in proximal or -C(sp 3 )-H bond, selective functionalization of distal bonds in amino acids remains a challenge. In recent years, transition metal catalyzed C-H functionalization has come up as a superior tool for C-H bond transformations. In this review, we intend to provide the reader a detailed overview of the current state of art in the utilization of site selective C-H functionalization technology for transformation of distal C-H bonds of amino acids and peptides. Various methods and synthetic tools have been discussed to deliver a broader idea about the field. A systematic compilation has been presented here to provide insights into the recent developments and future challenges in the field. Surface modification ; Amino acid ; Chemistry ; Combinatorial chemistry ; Nanotechnology ; Materials science ; Biochemistry Catalytic C–H Functionalization Methods ; Synthesis and Catalytic Reactions ; Chemical Synthesis and Analysis
Authors: Zhicheng Jin | Yi Li | Ke Li | Jiajing Zhou | Justin Yeung | Chuxuan Ling | Wonjun Yim | Tengyu He | Yong Cheng | Ming Xu | Matthew N. Creyer | Yu‐Ci Chang | Pavla Fajtová | Maurice Retout | Baiyan Qi | Shuzhou Li | Anthony J. O’Donoghue | Jesse V. Jokerst
Source: Angewandte Chemie International Edition (article, 2022-11-21)
Field: Materials Science — Biomaterials

Peptide Amphiphile Mediated Co‐assembly for Nanoplasmonic Sensing Aromatic interactions are commonly involved in the assembly of naturally occurring building blocks, and these interactions can be replicated in an artificial setting to produce functional materials. Here we describe a colorimetric biosensor using co-assembly experiments with plasmonic gold and surfactant-like peptides (SLPs) spanning a wide range of aromatic residues, polar stretches, and interfacial affinities. The SLPs programmed in DDD-(ZZ)<sub>x</sub> -FFPC self-assemble into higher-order structures in response to a protease and subsequently modulate the colloidal dispersity of gold leading to a colorimetric readout. Results show the strong aggregation propensity of the FFPC tail without polar DDD head. The SLPs were specific to the target protease, i.e., M<sup>pro</sup> , a biomarker for SARS-CoV-2. This system is a simple and visual tool that senses M<sup>pro</sup> in phosphate buffer, exhaled breath condensate, and saliva with detection limits of 15.7, 20.8, and 26.1 nM, respectively. These results may have value in designing other protease testing methods. Amphiphile ; Peptide ; Nanotechnology ; Self-assembly ; Chemistry ; Materials science ; Biophysics ; Biochemistry ; Copolymer ; Organic chemistry ; Biology ; Polymer Supramolecular Self-Assembly in Materials ; Advanced biosensing and bioanalysis techniques ; Chemical Synthesis and Analysis
Authors: Weike Ye | X. L. Lei | Muratahan Aykol | Joseph H. Montoya
Source: Scientific Data (article, 2022-06-14)
Field: Materials Science — Materials Chemistry

Novel inorganic crystal structures predicted using autonomous simulation agents We report a dataset of 96640 crystal structures discovered and computed using our previously published autonomous, density functional theory (DFT) based, active-learning workflow named CAMD (Computational Autonomy for Materials Discovery). Of these, 894 are within 1 meV/atom of the convex hull and 26826 are within 200 meV/atom of the convex hull. The dataset contains DFT-optimized pymatgen crystal structure objects, DFT-computed formation energies and phase stability calculations from the convex hull. It contains a variety of spacegroups and symmetries derived from crystal prototypes derived from known experimental compounds, and was generated from active learning campaigns of various chemical systems. This dataset can be used to benchmark future active-learning or generative efforts for structure prediction, to seed new efforts of experimental crystal structure discovery, or to construct new models of structure-property relationships. Computer science ; Biological system ; Chemistry ; Crystallography ; Biology Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Electronic and Structural Properties of Oxides
Authors: John C. Thomas | Antonio Rossi | Darian Smalley | Luca Francaviglia | Zhuohang Yu | Tianyi Zhang | Shalini Kumari | Joshua A. Robinson | Mauricio Terrones | Masa Ishigami | Eli Rotenberg | Edward S. Barnard | Archana Raja | Ed Wong | D. Frank Ogletree | Marcus M. Noack | Alexander Weber‐Bargioni
Source: npj Computational Materials (article, 2022-05-02)
Field: Materials Science — Materials Chemistry

Autonomous scanning probe microscopy investigations over WS2 and Au{111} Abstract Individual atomic defects in 2D materials impact their macroscopic functionality. Correlating the interplay is challenging, however, intelligent hyperspectral scanning tunneling spectroscopy (STS) mapping provides a feasible solution to this technically difficult and time consuming problem. Here, dense spectroscopic volume is collected autonomously via Gaussian process regression, where convolutional neural networks are used in tandem for spectral identification. Acquired data enable defect segmentation, and a workflow is provided for machine-driven decision making during experimentation with capability for user customization. We provide a means towards autonomous experimentation for the benefit of both enhanced reproducibility and user-accessibility. Hyperspectral investigations on WS 2 sulfur vacancy sites are explored, which is combined with local density of states confirmation on the Au{111} herringbone reconstruction. Chalcogen vacancies, pristine WS 2 , Au face-centered cubic, and Au hexagonal close-packed regions are examined and detected by machine learning methods to demonstrate the potential of artificial intelligence for hyperspectral STS mapping. Microscopy ; Scanning probe microscopy ; Materials science ; Scanning ion-conductance microscopy ; Nanotechnology ; Scanning electron microscope ; Scanning confocal electron microscopy ; Optics ; Physics ; Composite material 2D Materials and Applications ; Chalcogenide Semiconductor Thin Films ; Surface and Thin Film Phenomena
Authors: Kai Zhang | Huichun Zhang
Source: ACS ES&T Water (article, 2022-07-19)
Field: Materials Science — Materials Chemistry

Machine Learning Modeling of Environmentally Relevant Chemical Reactions for Organic Compounds Environmental chemical reactions have been frequently investigated for various purposes; however, it remains challenging to accurately model either the reaction kinetics or reaction pathways. Existing studies mostly model reaction kinetics with traditional quantitative structure–activity relationships (QSARs) or reaction pathways with reaction template methods; however, these approaches generally require extensive feature engineering or manual extraction of reaction templates. Recently, machine learning (ML) has become a promising tool for modeling chemical reactions as ML models can perform well and are powerful in using diverse chemical representations. This Review starts with a concise comparison of traditional and ML modeling approaches for chemical reactions, followed by a brief discussion of the status of and future needs in modeling environmental organic reactions. Data collection and data cleaning techniques for reaction kinetics and pathways are then discussed. We then summarize the advantages and limitations of commonly used chemical representations and feature selection techniques. Next, we critically review general ML model evaluation and interpretation processes and propose a three-step evaluation process, that is, comparisons with general metrics, baseline models, and existing models. Lastly, we explore ML modeling approaches for small data sets, including transfer learning and active learning, which have been successfully employed in many other fields, for future modeling of environmental chemical reactions. Computer science ; Biochemical engineering ; Process (computing) ; Artificial intelligence ; Template ; Chemical reaction ; Machine learning ; Biological system ; Chemistry ; Engineering Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Chemistry and Chemical Engineering