jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Bidyut Bikash Sarma | Florian Maurer | Dmitry E. Doronkin | Jan‐Dierk Grunwaldt
Source: Chemical Reviews (review, 2022-11-23)
Field: Materials Science — Materials Chemistry

Design of Single-Atom Catalysts and Tracking Their Fate Using <i>Operando</i> and Advanced X-ray Spectroscopic Tools The potential of <i>operando</i> X-ray techniques for following the structure, fate, and active site of single-atom catalysts (SACs) is highlighted with emphasis on a synergetic approach of both topics. X-ray absorption spectroscopy (XAS) and related X-ray techniques have become fascinating tools to characterize solids and they can be applied to almost all the transition metals deriving information about the symmetry, oxidation state, local coordination, and many more structural and electronic properties. SACs, a newly coined concept, recently gained much attention in the field of heterogeneous catalysis. In this way, one can achieve a minimum use of the metal, theoretically highest efficiency, and the design of only one active site-so-called single site catalysts. While single sites are not easy to characterize especially under operating conditions, XAS as local probe together with complementary methods (infrared spectroscopy, electron microscopy) is ideal in this research area to prove the structure of these sites and the dynamic changes during reaction. In this review, starting from their fundamentals, various techniques related to conventional XAS and X-ray photon in/out techniques applied to single sites are discussed with detailed mechanistic and <i>in situ</i>/<i>operando</i> studies. We systematically summarize the design strategies of SACs and outline their exploration with XAS supported by density functional theory (DFT) calculations and recent machine learning tools. X-ray absorption spectroscopy ; Chemistry ; Catalysis ; Transition metal ; Nanotechnology ; Absorption spectroscopy ; Density functional theory ; Electronic structure ; Atom (system on chip) ; Spectroscopy ; Computational chemistry ; Computer science ; Physics ; Materials science ; Optics Machine Learning in Materials Science ; Catalytic Processes in Materials Science ; Electrocatalysts for Energy Conversion
Authors: Matthias Scheffler | Martin Aeschlimann | M. Albrecht | Tristan Bereau | Hans‐Joachim Bungartz | Claudia Felser | Mark Greiner | Axel Groß | Christoph T. Koch | Kurt Kremer | Wolfgang E. Nagel | Markus Scheidgen | Christof Wöll | Claudia Draxl
Source: Nature (review, 2022-04-27)
Field: Materials Science — Materials Chemistry

FAIR data enabling new horizons for materials research The prosperity and lifestyle of our society are very much governed by achievements in condensed matter physics, chemistry and materials science, because new products for sectors such as energy, the environment, health, mobility and information technology (IT) rely largely on improved or even new materials. Examples include solid-state lighting, touchscreens, batteries, implants, drug delivery and many more. The enormous amount of research data produced every day in these fields represents a gold mine of the twenty-first century. This gold mine is, however, of little value if these data are not comprehensively characterized and made available. How can we refine this feedstock; that is, turn data into knowledge and value? For this, a FAIR (findable, accessible, interoperable and reusable) data infrastructure is a must. Only then can data be readily shared and explored using data analytics and artificial intelligence (AI) methods. Making data 'findable and AI ready' (a forward-looking interpretation of the acronym) will change the way in which science is carried out today. In this Perspective, we discuss how we can prepare to make this happen for the field of materials science. Prosperity ; Raw data ; Data science ; Computer science ; Interoperability ; Field (mathematics) ; Analytics ; Value (mathematics) ; Nanotechnology ; World Wide Web ; Political science Machine Learning in Materials Science ; Advanced X-ray and CT Imaging ; Scientific Computing and Data Management
Authors: Hui Liu | Yuyan Su | Xinchi Jiang | Jianqing Gao
Source: Drug Delivery and Translational Research (review, 2022-11-22)
Field: Materials Science — Biomaterials

Cell membrane-coated nanoparticles: a novel multifunctional biomimetic drug delivery system Drug delivery ; Nanotechnology ; Membrane ; Nanoparticle ; Targeted drug delivery ; Mechanism (biology) ; Cell ; Cell membrane ; Materials science ; Chemistry Nanoparticle-Based Drug Delivery ; Nanoplatforms for cancer theranostics ; Graphene and Nanomaterials Applications
Authors: Yilin Jiang | Jinfeng Dong | Hua‐Lu Zhuang | Jincheng Yu | Bin Su | Hezhang Li | Jun Pei | Fu‐Hua Sun | Min Zhou | Haihua Hu | Jingwei Li | Zhanran Han | Bo‐Ping Zhang | Takao Mori | Jing‐Feng Li
Source: Nature Communications (article, 2022-10-14)
Field: Materials Science — Materials Chemistry

Evolution of defect structures leading to high ZT in GeTe-based thermoelectric materials GeTe is a promising mid-temperature thermoelectric compound but inevitably contains excessive Ge vacancies hindering its performance maximization. This work reveals that significant enhancement in the dimensionless figure of merit (ZT) could be realized by defect structure engineering from point defects to line and plane defects of Ge vacancies. The evolved defects including dislocations and nanodomains enhance phonon scattering to reduce lattice thermal conductivity in GeTe. The accumulation of cationic vacancies toward the formation of dislocations and planar defects weakens the scattering against electronic carriers, securing the carrier mobility and power factor. This synergistic effect on electronic and thermal transport properties remarkably increases the quality factor. As a result, a maximum ZT > 2.3 at 648 K and a record-high average ZT (300-798 K) were obtained for Bi<sub>0.07</sub>Ge<sub>0.90</sub>Te in lead-free GeTe-based compounds. This work demonstrates an important strategy for maximizing the thermoelectric performance of GeTe-based materials by engineering the defect structures, which could also be applied to other thermoelectric materials. Materials science ; Thermoelectric effect ; Condensed matter physics ; Crystallographic defect ; Phonon scattering ; Phonon ; Thermal conductivity ; Thermoelectric materials ; Scattering ; Figure of merit ; Optoelectronics ; Composite material ; Thermodynamics ; Optics ; Physics Advanced Thermoelectric Materials and Devices ; Chalcogenide Semiconductor Thin Films ; Thermal properties of materials
Authors: Yi Luo | Saientan Bag | Orysia Zaremba | Adrian Cierpka | Jacopo Andreo | Stefan Wuttke | Pascal Friederich | Manuel Tsotsalas
Source: Angewandte Chemie International Edition (article, 2022-02-01)
Field: Chemistry — Inorganic Chemistry

MOF Synthesis Prediction Enabled by Automatic Data Mining and Machine Learning** Despite rapid progress in the field of metal-organic frameworks (MOFs), the potential of using machine learning (ML) methods to predict MOF synthesis parameters is still untapped. Here, we show how ML can be used for rationalization and acceleration of the MOF discovery process by directly predicting the synthesis conditions of a MOF based on its crystal structure. Our approach is based on: i) establishing the first MOF synthesis database via automatic extraction of synthesis parameters from the literature, ii) training and optimizing ML models by employing the MOF database, and iii) predicting the synthesis conditions for new MOF structures. The ML models, even at an initial stage, exhibit a good prediction performance, outperforming human expert predictions, obtained through a synthesis survey. The automated synthesis prediction is available via a web-tool on https://mof-synthesis.aimat.science. Computer science ; Machine learning ; Artificial intelligence ; Data mining Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; Catalysis and Oxidation Reactions
Authors: Jun Zhang | Sandrine Ricote | Peter Vang Hendriksen | Yunzhong Chen
Source: Advanced Functional Materials (article, 2022-03-04)
Field: Materials Science — Materials Chemistry

Advanced Materials for Thin‐Film Solid Oxide Fuel Cells: Recent Progress and Challenges in Boosting the Device Performance at Low Temperatures Abstract Solid oxide fuel cells (SOFCs) are efficient and fuel flexible electrochemical energy conversion devices that can power the future green society with regards to homes, cars, and even down to portable electronics. They do have the potential to become low cost, since no noble metals are used. Their broad commercialization, however, is hampered by the high operating temperatures of 700–900 ° C. Lowering the operating temperature of SOFCs is challenging as both the charge transport in the solid electrolyte and oxygen exchange reactions are thermally activated processes. Herein, the recent progress in the development of anode, electrolyte, and cathode materials to lower the operating temperature of SOFC below 600 ° C is summarized and the new opportunities, as well as challenges that remain to be solved, are discussed. The focus of this review is addressed to thin film SOFCs, sub‐micrometer SOFCs ( μ SOFCs) based on microelectromechanical systems, as well as devices based on proton‐conducting oxide electrolyte (protonic ceramic fuel cells), which are especially promising for powering portable devices. Materials science ; Boosting (machine learning) ; Nanotechnology ; Oxide ; Thin film ; Engineering physics ; Fuel cells ; Chemical engineering ; Metallurgy ; Computer science ; Artificial intelligence ; Engineering Advancements in Solid Oxide Fuel Cells ; Electronic and Structural Properties of Oxides ; Magnetic and transport properties of perovskites and related materials
Authors: Yu Lei | Tianyi Zhang | Yu‐Chuan Lin | Tomotaroh Granzier-Nakajima | George Bepete | Dorota A. Kowalczyk | Zhong Lin | Da Zhou | Thomas F. Schranghamer | Akhil Dodda | Amritanand Sebastian | Yifeng Chen | Yuanyue Liu | Geoffrey Pourtois | Thomas J. Kempa | Bruno Schuler | Mark T. Edmonds | Su Ying Quek | Ursula Wurstbauer | Stephen M. Wu | Nicholas R. Glavin | Saptarshi Das | Saroj P. Dash | Joan M. Redwing | Joshua A. Robinson | Mauricio Terrones
Source: ACS Nanoscience Au (review, 2022-09-16)
Field: Materials Science — Materials Chemistry

Graphene and Beyond: Recent Advances in Two-Dimensional Materials Synthesis, Properties, and Devices Since the isolation of graphene in 2004, two-dimensional (2D) materials research has rapidly evolved into an entire subdiscipline in the physical sciences with a wide range of emergent applications. The unique 2D structure offers an open canvas to tailor and functionalize 2D materials through layer number, defects, morphology, moiré pattern, strain, and other control knobs. Through this review, we aim to highlight the most recent discoveries in the following topics: theory-guided synthesis for enhanced control of 2D morphologies, quality, yield, as well as insights toward novel 2D materials; defect engineering to control and understand the role of various defects, including <i>in situ</i> and <i>ex situ</i> methods; and properties and applications that are related to moiré engineering, strain engineering, and artificial intelligence. Finally, we also provide our perspective on the challenges and opportunities in this fascinating field. Nanotechnology ; Graphene ; Materials science ; Engineering physics ; Engineering 2D Materials and Applications ; Graphene research and applications ; MXene and MAX Phase Materials
Authors: Qingchun Zhu | Yangxin Li | Fuyong Cao | Dong Qiu | Yao Yang | Jingya Wang | Huan Zhang | Tao Ying | Wenjiang Ding | Xiaoqin Zeng
Source: Nature Communications (article, 2022-10-03)
Field: Materials Science — Biomaterials

Towards development of a high-strength stainless Mg alloy with Al-assisted growth of passive film Magnesium alloys with high strength and excellent corrosion resistance are always sought-after in light-weighting structural components for automotive and aerospace applications. However, for most magnesium alloys that have a high specific strength, they usually have an inferior corrosion resistance and vice versa. In this work, we successfully develop a Mg-11Y-1Al (wt. %) alloy through conventional casting, solution treatment followed by extrusion. The overall properties of this alloy feature with a corrosion rate lower than 0.2 mm y<sup>-1</sup>, high yield strength of 350 MPa and moderate tensile elongation of 8%, the combination of which shows competitive advantage over other comparative magnesium alloys in the literature. It is found that a thin and dense protective film of Y<sub>2</sub>O<sub>3</sub>/Y(OH)<sub>3</sub> can be fast developed with the aid of Al<sub>2</sub>O<sub>3</sub>/Al(OH)<sub>3</sub> deposition to isolate this alloy from further attack of corrosion medium. Meanwhile, the refined grains, weak texture and activation of non-basal slip systems co-contribute to the high strength and good ductility. Our findings are expected to inspire the design of next-generation high performance magnesium alloys. Materials science ; Corrosion ; Ultimate tensile strength ; Alloy ; Magnesium ; Elongation ; Metallurgy ; Magnesium alloy ; Extrusion ; Slip (aerodynamics) ; Specific strength ; Casting ; Coating ; Ductility (Earth science) ; Texture (cosmology) ; Composite material ; Composite number Magnesium Alloys: Properties and Applications ; Aluminum Alloys Composites Properties ; Corrosion Behavior and Inhibition
Authors: Seokhyeong Lee | Ruoming Peng | Changming Wu | Mo Li
Source: Nature Communications (article, 2022-03-18)
Field: Materials Science — Polymers and Plastics

Programmable black phosphorus image sensor for broadband optoelectronic edge computing Image sensors with internal computing capability enable in-sensor computing that can significantly reduce the communication latency and power consumption for machine vision in distributed systems and robotics. Two-dimensional semiconductors have many advantages in realizing such intelligent vision sensors because of their tunable electrical and optical properties and amenability for heterogeneous integration. Here, we report a multifunctional infrared image sensor based on an array of black phosphorous programmable phototransistors (bP-PPT). By controlling the stored charges in the gate dielectric layers electrically and optically, the bP-PPT's electrical conductance and photoresponsivity can be locally or remotely programmed with 5-bit precision to implement an in-sensor convolutional neural network (CNN). The sensor array can receive optical images transmitted over a broad spectral range in the infrared and perform inference computation to process and recognize the images with 92% accuracy. The demonstrated bP image sensor array can be scaled up to build a more complex vision-sensory neural network, which will find many promising applications for distributed and remote multispectral sensing. Multispectral image ; Computer science ; Image sensor ; Artificial intelligence ; Convolutional neural network ; Machine vision ; Computer vision Transition Metal Oxide Nanomaterials ; Advanced Memory and Neural Computing ; 2D Materials and Applications
Authors: Felix Strieth‐Kalthoff | Frederik Sandfort | Marius Kühnemund | Felix Schäfer | Herbert Kuchen | Frank Glorius
Source: Angewandte Chemie International Edition (article, 2022-05-05)
Field: Materials Science — Materials Chemistry

Machine Learning for Chemical Reactivity: The Importance of Failed Experiments Assessing the outcomes of chemical reactions in a quantitative fashion has been a cornerstone across all synthetic disciplines. Classically approached through empirical optimization, data-driven modelling bears an enormous potential to streamline this process. However, such predictive models require significant quantities of high-quality data, the availability of which is limited: Main reasons for this include experimental errors and, importantly, human biases regarding experiment selection and result reporting. In a series of case studies, we investigate the impact of these biases for drawing general conclusions from chemical reaction data, revealing the utmost importance of "negative" examples. Eventually, case studies into data expansion approaches showcase directions to circumvent these limitations-and demonstrate perspectives towards a long-term data quality enhancement in chemistry. Cornerstone ; Computer science ; Process (computing) ; Quality (philosophy) ; Data science ; Experimental data ; Data quality ; Term (time) ; Management science ; Biochemical engineering ; Machine learning ; Artificial intelligence ; Epistemology ; Engineering ; Mathematics ; Operations management Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Metabolomics and Mass Spectrometry Studies
Authors: Georg Kustatscher | Tom Collins | Anne‐Claude Gingras | Tiannan Guo | Henning Hermjakob | Trey Ideker | Kathryn S. Lilley | Emma Lundberg | Edward M. Marcotte | Markus Ralser | Juri Rappsilber
Source: Nature Methods (article, 2022-05-09)
Field: Chemistry — Spectroscopy

Understudied proteins: opportunities and challenges for functional proteomics Computational biology ; Proteomics ; Function (biology) ; Protein function ; Biology ; Data science ; Posttranslational modification ; Bioinformatics ; Computer science ; Genetics ; Gene ; Biochemistry Advanced Proteomics Techniques and Applications ; Biotin and Related Studies ; Click Chemistry and Applications
Authors: Chirag R. Ratwani | Ali Reza Kamali | Amr M. Abdelkader
Source: Progress in Materials Science (review, 2022-08-23)
Field: Materials Science — Polymers and Plastics

Self-healing by Diels-Alder cycloaddition in advanced functional polymers: A review The ability of artificial materials to be healed efficiently, mimicking the living organisms, exhibits a great deal of potential advantages that can revolutionise the operation and maintenance of materials used in various applications. Such self-healable smart materials have been extensively researched in the last few decades, leading to the development of different physical and chemical synthesis approaches. Among these methods, chemical techniques based on reversible cycloadditions or disulfide bonding provide obvious advantages in terms of repeatability, which holds prime importance in determining the commerciality of the healing approach. This review compiles the recent advances in the field of self-healing polymers where the healing ability is introduced by reversible cycloaddition reactions while focusing mainly on the Diels-Alder (DA) reaction. DA is a [4 + 2] cycloaddition reaction where diene and dienophile pairs are used to fabricate thermally reversible crosslinked networks. These covalent bonds provide the necessary reversibility to the healing matrix and impart the desired strength to the polymeric material. There is a considerable body of recent literature where DA bonding has been employed either on its own or along with other healing mechanisms to impart self-healing to polymers. However, lack of a systematic review discussing these works makes it difficult for a beginner to cope with advancements in this field. Most early studies have focused on the healing stimuli and efficiency of healing in polymers but with this review, we would like to explore the healing thermodynamics governing the rupture–repair process in DA polymers along with the use of advanced spectroscopic techniques to study them and their applicability in thermosets, epoxy resins, biopolymers, and polymer nanocomposites. Novel applications for such advanced functional polymers, multifunctional healable polymers, and the outlook for future research, opportunities and challenges in the area are also discussed. Self-healing material ; Cycloaddition ; Self-healing ; Materials science ; Polymer ; Covalent bond ; Nanotechnology ; Diels–Alder reaction ; Smart material ; Polymer science ; Computer science ; Organic chemistry ; Composite material ; Chemistry ; Catalysis Polymer composites and self-healing ; Synthesis and properties of polymers ; Synthetic Organic Chemistry Methods
Authors: Daniil A. Boiko | Konstantin S. Kozlov | Julia V. Burykina | Valentina V. Ilyushenkova | Valentine P. Ananikov
Source: Journal of the American Chemical Society (article, 2022-08-08)
Field: Chemistry — Spectroscopy

Fully Automated Unconstrained Analysis of High-Resolution Mass Spectrometry Data with Machine Learning Mass spectrometry (MS) is a convenient, highly sensitive, and reliable method for the analysis of complex mixtures, which is vital for materials science, life sciences fields such as metabolomics and proteomics, and mechanistic research in chemistry. Although it is one of the most powerful methods for individual compound detection, complete signal assignment in complex mixtures is still a great challenge. The unconstrained formula-generating algorithm, covering the entire spectra and revealing components, is a "dream tool" for researchers. We present the framework for efficient MS data interpretation, describing a novel approach for detailed analysis based on deisotoping performed by gradient-boosted decision trees and a neural network that generates molecular formulas from the fine isotopic structure, approaching the long-standing inverse spectral problem. The methods were successfully tested on three examples: fragment ion analysis in protein sequencing for proteomics, analysis of the natural samples for life sciences, and study of the cross-coupling catalytic system for chemistry. Chemistry ; Mass spectrometry ; Proteomics ; Resolution (logic) ; Artificial neural network ; Artificial intelligence ; Computer science ; Chromatography Mass Spectrometry Techniques and Applications ; Metabolomics and Mass Spectrometry Studies ; Analytical Chemistry and Chromatography
Authors: Wei Tan | Shaohua Xie | Duy Le | Weijian Diao | Meiyu Wang | Ke-Bin Low | Dave Austin | Sampyo Hong | Fei Gao | Lin Dong | Lu Ma | Steven N. Ehrlich | Talat S. Rahman | Fudong Liu
Source: Nature Communications (article, 2022-11-18)
Field: Materials Science — Materials Chemistry

Fine-tuned local coordination environment of Pt single atoms on ceria controls catalytic reactivity Constructing single atom catalysts with fine-tuned coordination environments can be a promising strategy to achieve satisfactory catalytic performance. Herein, via a simple calcination temperature-control strategy, CeO<sub>2</sub> supported Pt single atom catalysts with precisely controlled coordination environments are successfully fabricated. The joint experimental and theoretical analysis reveals that the Pt single atoms on Pt<sub>1</sub>/CeO<sub>2</sub> prepared at 550 °C (Pt/CeO<sub>2</sub>-550) are mainly located at the edge sites of CeO<sub>2</sub> with a Pt-O coordination number of ca. 5, while those prepared at 800 °C (Pt/CeO<sub>2</sub>-800) are predominantly located at distorted Ce substitution sites on CeO<sub>2</sub> terrace with a Pt-O coordination number of ca. 4. Pt/CeO<sub>2</sub>-550 and Pt/CeO<sub>2</sub>-800 with different Pt<sub>1</sub>-CeO<sub>2</sub> coordination environments exhibit a reversal of activity trend in CO oxidation and NH<sub>3</sub> oxidation due to their different privileges in reactants activation and H<sub>2</sub>O desorption, suggesting that the catalytic performance of Pt single atom catalysts in different target reactions can be maximized by optimizing their local coordination structures. Reactivity (psychology) ; Catalysis ; Materials science ; Chemistry ; Chemical physics ; Medicine ; Organic chemistry Catalytic Processes in Materials Science ; Electrocatalysts for Energy Conversion ; Advanced Nanomaterials in Catalysis