jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Ning Xu | Xue‐Liang Peng | Hao‐Ru Li | Jiaxuan Liu | Ji‐Si‐Yu Cheng | Xin-Ya Qi | Shao-Jie Ye | Hai‐Lun Gong | Xiaohong Zhao | Jiangming Yu | Guo-hua Xu | Dai‐Xu Wei
Source: Frontiers in Nutrition (review, 2021-08-24)
Field: Materials Science — Biomaterials

Marine-Derived Collagen as Biomaterials for Human Health Collagen is a kind of biocompatible protein material, which is widely used in medical tissue engineering, drug delivery, cosmetics, food and other fields. Because of its wide source, low extraction cost and good physical and chemical properties, it has attracted the attention of many researchers in recent years. However, the application of collagen derived from terrestrial organisms is limited due to the existence of diseases, religious beliefs and other problems. Therefore, exploring a wider range of sources of collagen has become one of the main topics for researchers. Marine-derived collagen (MDC) stands out because it comes from a variety of sources and avoids issues such as religion. On the one hand, this paper summarized the sources, extraction methods and characteristics of MDC, and on the other hand, it summarized the application of MDC in the above fields. And on the basis of the review, we found that MDC can not only be extracted from marine organisms, but also from the wastes of some marine organisms, such as fish scales. This makes further use of seafood resources and increases the application prospect of MDC. Biocompatible material ; Biochemical engineering ; Cosmetics ; Nanotechnology ; Fish <Actinopterygii> ; Marine conservation ; Computer science ; Variety (cybernetics) ; Drug delivery ; Risk analysis (engineering) ; Environmental science ; Engineering ; Chemistry ; Fishery ; Biology ; Biomedical engineering ; Materials science ; Environmental resource management ; Business ; Artificial intelligence Collagen: Extraction and Characterization ; Protein Hydrolysis and Bioactive Peptides ; Bone and Dental Protein Studies
Authors: Ying Lou | Xinyu Hao | Lei Liao | Kaiyou Zhang | Shuoping Chen | Ziyuan Li | Jun Ou | Aimiao Qin | Li Zhou
Source: Nano Select (article, 2021-01-15)
Field: Materials Science — Materials Chemistry

Recent advances of biomass carbon dots on syntheses, characterization, luminescence mechanism, and sensing applications Abstract Carbon dots have attracted much attention due to their high fluorescence intensity, easy modification, good stability, and biocompatibility. However, the realization of low‐cost mass production of high‐quality carbon dots still faces great challenges. Biomass is of non‐toxic and environmentally friendly organism, but a lot of biomass is treated as waste for burning and landfill at present, causing irreparable pollution to the environment. In fact, many biomass resources are ideal candidates for preparing carbon dots. This review focuses on carbon dots including carbon quantum dots (CQDs) and graphene quantum dots (GQDs) which using biomass as carbon source on the aspects of plants and their derivatives, animals and their derivatives and municipal waste. The characterization of the structure and composition of biomass carbon dots, the regulation of fluorescence color and the methods of improving quantum yield (QY) including heteroatom doping and surface modification are introduced in detail. Moreover, biomass carbon dots for detecting metal ions and non‐metal molecules and their quenching mechanism are emphatically introduced in addition to summarizing the luminescence mechanism, and some promising prospects and challenges in this uplifting field are discussed. Biomass (ecology) ; Quantum dot ; Carbon fibers ; Nanotechnology ; Materials science ; Graphene ; Environmental pollution ; Quantum yield ; Carbon quantum dots ; Fluorescence ; Environmental science ; Environmental protection ; Ecology ; Physics ; Composite material Carbon and Quantum Dots Applications ; Nanocluster Synthesis and Applications ; Quantum Dots Synthesis And Properties
Authors: Suyun Zhu | Mattia Scardamaglia | Jan Kundsen | R. Sankari | Hamed Tarawneh | Robert H. Temperton | L. Pickworth | Filippo Cavalca | Chunlei Wang | Héloïse Tissot | Jonas Weissenrieder | Benjamin Hagman | Johan Gustafson | Sarp Kaya | Fredrik Lindgren | Ida Källquist | Julia Maibach | Maria Hahlin | Virgínia Boix | Tamires Gallo | Foqia Rehman | Giulio D’Acunto | Joachim Schnadt | Andrey Shavorskiy
Source: Journal of Synchrotron Radiation (article, 2021-02-12)
Field: Materials Science — Surfaces, Coatings and Films

HIPPIE: a new platform for ambient-pressure X-ray photoelectron spectroscopy at the MAX IV Laboratory HIPPIE is a soft X-ray beamline on the 3 GeV electron storage ring of the MAX IV Laboratory, equipped with a novel ambient-pressure X-ray photoelectron spectroscopy (APXPS) instrument. The endstation is dedicated to performing in situ and operando X-ray photoelectron spectroscopy experiments in the presence of a controlled gaseous atmosphere at pressures up to 30 mbar [1 mbar = 100 Pa] as well as under ultra-high-vacuum conditions. The photon energy range is 250 to 2200 eV in planar polarization and with photon fluxes >10<sup>12</sup> photons s<sup>-1</sup> (500 mA ring current) at a resolving power of greater than 10000 and up to a maximum of 32000. The endstation currently provides two sample environments: a catalysis cell and an electrochemical/liquid cell. The former allows APXPS measurements of solid samples in the presence of a gaseous atmosphere (with a mixture of up to eight gases and a vapour of a liquid) and simultaneous analysis of the inlet/outlet gas composition by online mass spectrometry. The latter is a more versatile setup primarily designed for APXPS at the solid-liquid (dip-and-pull setup) or liquid-gas (liquid microjet) interfaces under full electrochemical control, and it can also be used as an open port for ad hoc-designed non-standard APXPS experiments with different sample environments. The catalysis cell can be further equipped with an IR reflection-absorption spectrometer, allowing for simultaneous APXPS and IR spectroscopy of the samples. The endstation is set up to easily accommodate further sample environments. X-ray photoelectron spectroscopy ; Ambient pressure ; Analytical Chemistry (journal) ; Chemistry ; Spectroscopy ; Optics ; Materials science ; Physics ; Nuclear magnetic resonance Electron and X-Ray Spectroscopy Techniques ; Electronic and Structural Properties of Oxides ; Semiconductor materials and devices
Authors: Xinyu Chen | Yufeng Xie | Yaochen Sheng | Hongwei Tang | Zeming Wang | Yu Wang | Yin Wang | Fuyou Liao | Jingyi Ma | Xiaojiao Guo | Ling Tong | Hanqi Liu | Hao Liu | Tianxiang Wu | Jiaxin Cao | Sitong Bu | Hui Shen | Fuyu Bai | Daming Huang | Jianan Deng | Antoine Riaud | Zihan Xu | Chenjian Wu | Shiwei Xing | Ye Lü | Shunli Ma | Zhengzong Sun | Zhongyin Xue | Zengfeng Di | Xiao Gong | David Wei Zhang | Peng Zhou | Jing Wan | Wenzhong Bao
Source: Nature Communications (article, 2021-10-12)
Field: Materials Science — Materials Chemistry

Wafer-scale functional circuits based on two dimensional semiconductors with fabrication optimized by machine learning Triggered by the pioneering research on graphene, the family of two-dimensional layered materials (2DLMs) has been investigated for more than a decade, and appealing functionalities have been demonstrated. However, there are still challenges inhibiting high-quality growth and circuit-level integration, and results from previous studies are still far from complying with industrial standards. Here, we overcome these challenges by utilizing machine-learning (ML) algorithms to evaluate key process parameters that impact the electrical characteristics of MoS<sub>2</sub> top-gated field-effect transistors (FETs). The wafer-scale fabrication processes are then guided by ML combined with grid searching to co-optimize device performance, including mobility, threshold voltage and subthreshold swing. A 62-level SPICE modeling was implemented for MoS<sub>2</sub> FETs and further used to construct functional digital, analog, and photodetection circuits. Finally, we present wafer-scale test FET arrays and a 4-bit full adder employing industry-standard design flows and processes. Taken together, these results experimentally validate the application potential of ML-assisted fabrication optimization for beyond-silicon electronic materials. Wafer ; Fabrication ; Semiconductor ; Semiconductor device fabrication ; Scale (ratio) ; Electronic circuit ; Nanotechnology ; Materials science ; Computer science ; Optoelectronics ; Electrical engineering ; Engineering ; Physics ; Medicine 2D Materials and Applications ; Advanced Memory and Neural Computing ; Graphene research and applications
Authors: Jure Berce | Matevž Zupančič | Matic Može | Iztok Golobič
Source: Processes (review, 2021-08-01)
Field: Materials Science — Biomaterials

A Review of Crystallization Fouling in Heat Exchangers A vast majority of heat exchangers suffer from unwanted deposition of material on the surface, which severely inhibits their performance and thus marks one of the biggest challenges in heat transfer. Despite numerous scientific investigations, prediction and prevention of fouling remain unresolved issues in process engineering and are responsible for large economic losses and environmental damage. This review article focuses specifically on crystallization fouling, providing a comprehensive overview of the state-of-the-art of fouling in heat exchangers. The fundamentals of the topic are discussed, as the term fouling resistance is introduced along with distinct fouling behaviour, observed in laboratory and industrial environments. Insight into subsequent phases of the fouling process is provided, along with the accompanying microscale events. Furthermore, the effects of fluid composition, temperature, flow velocity, surface condition, nucleate boiling and composite fouling are comprehensively discussed. Fouling modelling is systematically reviewed, from the early work of Kern and Seaton to recently used artificial neural networks and computational fluid dynamics. Finally, the most common fouling mitigation approaches are presented, including design considerations and various on-line strategies, as well as off-line cleaning. According to our review, several topics require further study, such as the initial stage of crystal formation, the effects of ageing, the interplay of two or more fouling mechanisms and the underlying phenomena of several mitigation strategies. Fouling ; Heat exchanger ; Heat transfer ; Process engineering ; Microscale chemistry ; Materials science ; Environmental science ; Mechanical engineering ; Biochemical engineering ; Engineering ; Chemistry ; Mechanics ; Mathematics ; Physics Calcium Carbonate Crystallization and Inhibition ; nanoparticles nucleation surface interactions ; Crystallization and Solubility Studies
Authors: Jingyuan Qiao | Lingqiao Kong | Shaokang Xu | Kaixian Lin | Wei He | Meng Ni | Qiushi Ruan | Peigen Zhang | Ying Liu | Wei Zhang | Long Pan | ZhengMing Sun
Source: Energy storage materials (article, 2021-09-28)
Field: Materials Science — Materials Chemistry

Research progress of MXene-based catalysts for electrochemical water-splitting and metal-air batteries MXenes ; Electrocatalyst ; Bifunctional ; Oxygen evolution ; Materials science ; Water splitting ; Catalysis ; Oxygen reduction reaction ; Electrochemistry ; Nanotechnology ; Metal ; Chemical engineering ; Electrode ; Chemistry ; Physical chemistry ; Metallurgy MXene and MAX Phase Materials ; Advanced Photocatalysis Techniques ; Advanced Memory and Neural Computing
Authors: Zhaojue Lan | Min-Hsuan Lee | Furong Zhu
Source: Advanced Intelligent Systems (article, 2021-11-21)
Field: Materials Science — Polymers and Plastics

Recent Advances in Solution‐Processable Organic Photodetectors and Applications in Flexible Electronics Organic photodetectors (OPDs) are promising for applications in flexible electronics due to their advantages of excellent photodetection performance, cost‐effective solution‐fabrication capability, flexible device design, and adaptivity to manufacturability. This review outlines the recent advances in the development of high‐performance OPDs and their applications in flexible electronics. The approaches to developing different noise reduction methods, filter‐free spectral selective detection, flexible OPDs, and scale‐up production of flexible OPDs through solution‐fabrication processes are discussed. Applications of the OPD technology ultimately result in the materialization of wearable units, flexible and compact information sensors at commercially viable costs, including wearable health self‐monitoring devices, flexible optical communication systems, and flexible large‐area image sensors. Electronics ; Photodetection ; Photodetector ; Design for manufacturability ; Wearable computer ; Wearable technology ; Computer science ; Photodiode ; Flexible electronics ; Materials science ; Nanotechnology ; Embedded system ; Electrical engineering ; Engineering ; Optoelectronics Conducting polymers and applications ; Advanced Sensor and Energy Harvesting Materials ; Organic Electronics and Photovoltaics
Authors: Hao Wang | Biaohua Chen | Di‐Jia Liu
Source: Advanced Materials (review, 2021-05-13)
Field: Chemistry — Inorganic Chemistry

Metal–Organic Frameworks and Metal–Organic Gels for Oxygen Electrocatalysis: Structural and Compositional Considerations Increasing demand for sustainable and clean energy is calling for the next-generation energy conversion and storage technologies such as fuel cells, water electrolyzers, CO<sub>2</sub> /N<sub>2</sub> reduction electrolyzers, metal-air batteries, etc. All these electrochemical processes involve oxygen electrocatalysis. Boosting the intrinsic activity and the active-site density through rational design of metal-organic frameworks (MOFs) and metal-organic gels (MOGs) as precursors represents a new approach toward improving oxygen electrocatalysis efficiency. MOFs/MOGs afford a broad selection of combinations between metal nodes and organic linkers and are known to produce electrocatalysts with high surface areas, variable porosity, and excellent activity after pyrolysis. Some recent studies on MOFs/MOGs for oxygen electrocatalysis and their new perspectives in synthesis, characterization, and performance are discussed. New insights on the structural and compositional design in MOF/MOG-derived oxygen electrocatalysts are summarized. Critical challenges and future research directions are also outlined. Electrocatalyst ; Materials science ; Metal-organic framework ; Metal ; Oxygen evolution ; Oxygen ; Oxygen reduction ; Nanotechnology ; Inorganic chemistry ; Chemical engineering ; Electrochemistry ; Metallurgy ; Organic chemistry ; Electrode ; Physical chemistry ; Chemistry Metal-Organic Frameworks: Synthesis and Applications ; Electrocatalysts for Energy Conversion ; Machine Learning in Materials Science
Authors: Tiago F. Machado | M. Elisa Silva Serra | Dina Murtinho | Artur J. M. Valente | Mu. Naushad
Source: Polymers (review, 2021-03-22)
Field: Materials Science — Materials Chemistry

Covalent Organic Frameworks: Synthesis, Properties and Applications—An Overview Covalent Organic Frameworks (COFs) are an exciting new class of microporous polymers with unprecedented properties in organic material chemistry. They are generally built from rigid, geometrically defined organic building blocks resulting in robust, covalently bonded crystalline networks that extend in two or three dimensions. By strategically combining monomers with specific structures and properties, synthesized COF materials can be fine-tuned and controlled at the atomic level, with unparalleled precision on intrapore chemical environment; moreover, the unusually high pore accessibility allows for easy post-synthetic pore wall modification after the COF is synthesized. Overall, COFs combine high, permanent porosity and surface area with high thermal and chemical stability, crystallinity and customizability, making them ideal candidates for a myriad of promising new solutions in a vast number of scientific fields, with widely varying applications such as gas adsorption and storage, pollutant removal, degradation and separation, advanced filtration, heterogeneous catalysis, chemical sensing, biomedical applications, energy storage and production and a vast array of optoelectronic solutions. This review attempts to give a brief insight on COF history, the overall strategies and techniques for rational COF synthesis and post-synthetic functionalization, as well as a glance at the exponentially growing field of COF research, summarizing their main properties and introducing the numerous technological and industrial state of the art applications, with noteworthy examples found in the literature. Nanotechnology ; Covalent bond ; Materials science ; Surface modification ; Covalent organic framework ; Crystallinity ; Microporous material ; Polymer ; Porous medium ; Porosity ; Chemical engineering ; Chemistry ; Organic chemistry ; Composite material Covalent Organic Framework Applications ; Metal-Organic Frameworks: Synthesis and Applications ; Advanced Photocatalysis Techniques
Authors: J.F. Durodola
Source: Progress in Materials Science (article, 2021-04-01)
Field: Materials Science — Materials Chemistry

Machine learning for design, phase transformation and mechanical properties of alloys Artificial intelligence ; Machine learning ; Interpretation (philosophy) ; Computer science ; Materials science ; Mechanical engineering ; Nanotechnology ; Industrial engineering ; Management science ; Engineering Machine Learning in Materials Science ; Additive Manufacturing Materials and Processes ; Titanium Alloys Microstructure and Properties
Authors: Yao Shi | Paloma L. Prieto | Tara Zepel | Shad Grunert | Jason E. Hein
Source: Accounts of Chemical Research (article, 2021-01-20)
Field: Materials Science — Materials Chemistry

Automated Experimentation Powers Data Science in Chemistry Data science has revolutionized chemical research and continues to break down barriers with new interdisciplinary studies. The introduction of computational models and machine learning (ML) algorithms in combination with automation and traditional experimental techniques has enabled scientific advancement across nearly every discipline of chemistry, from materials discovery, to process optimization, to synthesis planning. However, predictive tools powered by data science are only as good as their data sets and, currently, many of the data sets used to train models suffer from several limitations, including being sparse, limited in scope and requiring human curation. Likewise, computational data faces limitations in terms of accurate modeling of nonideal systems and can suffer from low translation fidelity from simulation to real conditions. The lack of diverse data and the need to be able to test it experimentally reduces both the accuracy and scope of the predictive models derived from data science. This Account contextualizes the need for more complex and diverse experimental data and highlights how the seamless integration of robotics, machine learning, and data-rich monitoring techniques can be used to access it with minimal human labor.We propose three broad categories of data in chemistry: data on fundamental properties, data on reaction outcomes, and data on reaction mechanics. We highlight flexible, automated platforms that can be deployed to acquire and leverage these data. The first platform combines solid- and liquid-dosing modules with computer vision to automate solubility screening, thereby gathering fundamental data that are necessary for almost every experimental design. Using computer vision offers the additional benefit of creating a visual record, which can be referenced and used to further interrogate and gain insight on the data collected. The second platform iteratively tests reaction variables proposed by a ML algorithm in a closed-loop fashion. Experimental data related to reaction outcomes are fed back into the algorithm to drive the discovery and optimization of new materials and chemical processes. The third platform uses automated process analytical technology to gather real-time data related to reaction kinetics. This system allows the researcher to directly interrogate the reaction mechanisms in granular detail to determine exactly how and why a reaction proceeds, thereby enabling reaction optimization and deployment. Computer science ; Leverage (statistics) ; Data science ; Automation ; Artificial intelligence ; Machine learning ; Scope (computer science) ; Experimental data ; Engineering Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Innovative Microfluidic and Catalytic Techniques Innovation
Authors: Peter C. Sherrell | Marco Fronzi | Nick A. Shepelin | Alexander Corletto | David A. Winkler | Michael J. Ford | Joseph G. Shapter | Amanda Ellis
Source: Chemical Society Reviews (review, 2021-12-21)
Field: Materials Science — Materials Chemistry

A bright future for engineering piezoelectric 2D crystals The piezoelectric effect, mechanical-to-electrical and electrical-to-mechanical energy conversion, is highly beneficial for functional and responsive electronic devices. To fully exploit this property, miniaturization of piezoelectric materials is the subject of intense research. Indeed, select atomically thin 2D materials strongly exhibit the piezoelectric effect. The family of 2D crystals consists of over 7000 chemically distinct members that can be further manipulated in terms of strain, functionalization, elemental substitution (<i>i.e.</i> Janus 2D crystals), and defect engineering to induce a piezoelectric response. Additionally, most 2D crystals can stack with other similar or dissimilar 2D crystals to form a much greater number of complex 2D heterostructures whose properties are quite different to those of the individual constituents. The unprecedented flexibility in tailoring 2D crystal properties, coupled with their minimal thickness, make these emerging highly attractive for advanced piezoelectric applications that include pressure sensing, piezocatalysis, piezotronics, and energy harvesting. This review summarizes literature on piezoelectricity, particularly out-of-plane piezoelectricity, in the vast family of 2D materials as well as their heterostructures. It also describes methods to induce, enhance, and control the piezoelectric properties. The volume of data and role of machine learning in predicting piezoelectricity is discussed in detail, and a prospective outlook on the 2D piezoelectric field is provided. Piezoelectricity ; Materials science ; Miniaturization ; Piezoelectric coefficient ; Stack (abstract data type) ; Nanotechnology ; Heterojunction ; Optoelectronics ; Engineering physics ; Computer science ; Composite material ; Physics 2D Materials and Applications ; MXene and MAX Phase Materials ; Advanced Sensor and Energy Harvesting Materials