jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Ziao Wang | Xi Zhu
Source: ACS Applied Nano Materials (article, 2024-03-12)
Field: Materials Science — Materials Chemistry

Enhancing Nanocrystal Synthesis: A Comparative Study of Online Artificial Intelligence Optimization and Offline High-Throughput Experimentation in Chemical Material Discovery The integration of artificial intelligence (AI) with high-throughput experimentation (HTE) has revolutionized chemical material discovery, accelerating the optimization process with an unprecedented efficiency and precision. This study conducts a detailed comparison of online AI and offline HTE approaches for chemical experimentation. Focusing on nanocrystal synthesis as an example application, online AI optimization is found to achieve superior performance and sustainability compared to conventional HTE methods that sweep larger experimental spaces. Specifically, for synthesizing NaYF4:Yb/Tm nanocrystals, offline HTE reaches better quality metrics, a good efficiency, and high carbon emissions, while online AI reaches similar quality and consumes far fewer raw materials with a better efficiency and low carbon emission. These findings spotlight AI’s potential to enhance the precision of chemical experiments while reducing environmental impacts through sustainable management of resources. Throughput ; Nanocrystal ; Computer science ; Process engineering ; Process (computing) ; Biochemical engineering ; Nanotechnology ; Materials science ; Engineering Machine Learning in Materials Science ; Artificial Intelligence in Healthcare and Education ; Electronic and Structural Properties of Oxides
Authors: Fangling Yang | Ryuhei Sato | Eric Jianfeng Cheng | Kazuaki Kisu | Qian Wang | Xue Jia | Shin‐ichi Orimo | Hao Li
Source: Dian hua xue/Dian huaxue (article, 2024-07-26)
Field: Materials Science — Materials Chemistry

Data-Driven Viewpoint for Developing Next-Generation Mg-Ion Solid-State Electrolytes Magnesium (Mg) is a promising alternative to lithium (Li) in solid-state batteries due to its abundance and high theoretical volumetric capacity. However, the sluggish Mg-ion conduction in the lattice of solid-state electrolytes (SSEs) is one of the key challenges that hamper the development of Mg-ion solid-state batteries. Though various Mg-ion SSEs have been reported in recent years, key insights are hard to be derived from a single literature report. Besides, the structure-performance relationships of Mg-ion SSEs need to be further unraveled to provide a more precise design guideline for SSEs. In this Viewpoints article, we analyze the structural characteristics of the Mg-based SSEs with high ionic conductivity reported in the last four decades based upon data mining - we provide big-data-derived insights into the challenges and opportunities in developing next-generation Mg-ion SSEs. Electrolyte ; Solid-state ; Ion ; Materials science ; Computer science ; Engineering physics ; Chemistry ; Engineering ; Electrode ; Physical chemistry Machine Learning in Materials Science ; Inorganic Fluorides and Related Compounds ; X-ray Diffraction in Crystallography
Authors: X. Jiang | Xiaoya Chen | Quanan Li | Dongzhen Wang | Zheng Wu
Source: Materials Today Communications (article, 2024-09-04)
Field: Materials Science — Biomaterials

Application and research progress of first principles calculation in magnesium alloys Materials science ; Magnesium ; Metallurgy ; Thermodynamics Magnesium Alloys: Properties and Applications ; Aluminum Alloys Composites Properties ; Advanced Welding Techniques Analysis
Authors: Guiming Liu | Huang Zhao | Jiujie Xu | Tiesong Lin | Bowen Zhang | Peng He
Source: Nanomaterials (article, 2024-06-24)
Field: Materials Science — Electronic, Optical and Magnetic Materials

MnO2 Nanoparticles Decorated PEDOT:PSS for High Performance Stretchable and Transparent Supercapacitors With the swift advancement of wearable electronics and artificial intelligence, the integration of electronic devices with the human body has advanced significantly, leading to enhanced real-time health monitoring and remote disease diagnosis. Despite progress in developing stretchable materials with skin-like mechanical properties, there remains a need for materials that also exhibit high optical transparency. Supercapacitors, as promising energy storage devices, offer advantages such as portability, long cycle life, and rapid charge/discharge rates, but achieving high capacity, stretchability, and transparency simultaneously remains challenging. This study combines the stretchable, transparent polymer PEDOT:PSS with MnO<sub>2</sub> nanoparticles to develop high-performance, stretchable, and transparent supercapacitors. PEDOT:PSS films were deposited on a PDMS substrate using a spin-coating method, followed by electrochemical deposition of MnO<sub>2</sub> nanoparticles. This method ensured that the nanosized MnO<sub>2</sub> particles were uniformly distributed, maintaining the transparency and stretchability of PEDOT:PSS. The resulting PEDOT:PSS/MnO<sub>2</sub> nanoparticle electrodes were gathered into a symmetric device using a LiCl/PVA gel electrolyte, achieving an areal capacitance of 1.14 mF cm<sup>-2</sup> at 71.2% transparency and maintaining 89.92% capacitance after 5000 cycles of 20% strain. This work presents a scalable and economical technique to manufacturing supercapacitors that combine high capacity, transparency, and mechanical stretchability, suggesting potential applications in wearable electronics. PEDOT:PSS ; Supercapacitor ; Materials science ; Nanoparticle ; Nanotechnology ; Capacitance ; Layer (electronics) ; Electrode ; Chemistry Supercapacitor Materials and Fabrication ; Conducting polymers and applications ; Advancements in Battery Materials
Authors: Qamar Salamat | Zinar Pınar Gümüş | Mustafa Soylak
Source: TrAC Trends in Analytical Chemistry (article, 2024-11-16)
Field: Chemistry — Analytical Chemistry

Recent developments and applications of artificial intelligence in solid/liquid extraction studies Artificial intelligence has great potential in extraction processes, and these technologies greatly facilitate the transformation of data into meaningful information. This review highlights inference studies using artificial intelligence and provides an overview of the types of algorithms and advanced chemometric tools used in these studies. The use of artificial intelligence, which is mostly used in classification, origin determination, and studies with large data sets, is still very limited in SPE and LPME research and investigations. This review examined SPE and LPME studies for different analytes using keywords in other algorithms under the main title of artificial intelligence. • Solid and liquid phase extraction studies using artificial intelligence were investigated. • The advantages of artificial intelligence in extraction were demonstrated. • The importance of artificial intelligence in data analysis and extraction in chemistry was emphasized. Extraction (chemistry) ; Computer science ; Artificial intelligence ; Chromatography ; Chemistry Analytical chemistry methods development ; Analytical Chemistry and Chromatography ; Geochemistry and Geologic Mapping
Authors: M. A. Shadab Siddiqui | Md. Abu Mowazzem Hossain | Ramisa Ferdous | M.S. Rabbi | Sidra Abid
Source: Results in Materials (article, 2024-12-22)
Field: Materials Science — Materials Chemistry

An extensive review on bibliometric analysis of carbon nanostructure reinforced composites The rapid evolution of the mechanical industry necessitates reliable and innovative materials. Metal matrix composites (MMCs) have emerged as a leading contender for performing vital roles in this field. Carbon nanostructures, such as graphene and carbon nanotubes (CNTs), are particularly well-suited as reinforcement materials in MMCs. It has been found by recent experimental studies that incorporating CNTs and graphene as reinforcements into metal matrix composites, such as aluminum, magnesium, titanium, nickel, and copper matrices, can significantly enhance the mechanical, thermal, and tribological properties of these materials. This is achieved through various mechanisms, including the restriction of grain growth, hindrance of dislocations, load transfer at interfaces, and mitigation of thermal expansion mismatch. The precise reinforcement and optimization of fabrication techniques have opened up new avenues for achieving uniform nanostructure dispersion and strong interfacial bonding, leading to substantial improvements in quantitative properties. Such advancements in material science hold great promise for the development of high-performance materials with enhanced properties that are vital for various applications, including aerospace, automotive, biomedical, and beyond. The addition of low-carbon nanostructures to polymer matrix, ceramic, and biocomposite systems has also been observed to elicit noteworthy multifunctional improvements. Reinforcing collagen with CNT fibers leads to better mechanical and electrical performance compared to using collagen alone. This critical review provides an insightful and data-driven analysis of the current state of carbon nanostructure (CNTs/graphene)-reinforced metal matrix and biocomposites based on an extensive literature evaluation. The review includes an in-depth examination of the optimized synthesis and processing techniques for CNTs and graphene MMCs, highlighting the impact of reinforcement on their mechanical, thermal conductivity, electrical conductivity, and functional properties. Continued work refining fabrication methods fully leverages their potent multi-functional enhancement capabilities. Composite material ; Materials science ; Nanostructure ; Carbon fibers ; Nanotechnology ; Composite number Carbon Nanotubes in Composites ; Graphene and Nanomaterials Applications ; Nanotechnology research and applications
Authors: Nate Hoxie | D. Calabrese | Zina Itkin | Glenn Gomba | Min Shen | Meghav Verma | John Janiszewski | Jonathan H. Shrimp | Kelli M. Wilson | Sam Michael | Matthew Hall | Lyle Burton | Tom Covey | Chang Liu
Source: SLAS TECHNOLOGY (article, 2024-10-19)
Field: Chemistry — Spectroscopy

High-resolution acoustic ejection mass spectrometry for high-throughput library screening An approach is described for high-throughput quality assessment of drug candidate libraries using high-resolution acoustic ejection mass spectrometry (AEMS). Sample introduction from 1536-well plates is demonstrated for this application using 2.5 nL acoustically dispensed sample droplets into an Open Port Interface (OPI) with pneumatically assisted electrospray ionization at a rate of one second per sample. Both positive and negative ionization are shown to be essential to extend the compound coverage of this protease inhibitor-focused library. Specialized software for efficiently interpreting this data in 1536-well format is presented. A new high-throughput method for quantifying the concentration of the components (HTQuant) is proposed that neither requires adding an internal standard to each well nor further encumbers the high-throughput workflow. This approach for quantitation requires highly reproducible peak areas, which is shown to be consistent within 4.4 % CV for a 1536-well plate analysis. An approach for troubleshooting the workflow based on the background ion current signal is also presented. The AEMS data is compared to the industry standard LC/PDA/ELSD/MS approach and shows similar coverage but at 180-fold greater throughput. Despite the same ionization process, both methods confirmed the presence of a small percentage of compounds in wells that the other did not. The data for this relatively small, focused library is compared to a larger, more chemically diverse library to indicate that this approach can be more generally applied beyond this single case study. This capability is particularly timely considering the growing implementation of artificial intelligence strategies that require the input of large amounts of high-quality data to formulate predictions relevant to the drug discovery process. The molecular structures of the 872-compound library analyzed here are included to begin the process of correlating molecular structures with ionization efficiency and other parameters as an initial step in this direction. Throughput ; Mass spectrometry ; High resolution ; Chemistry ; Computer science ; Analytical Chemistry (journal) ; Chromatography ; Telecommunications ; Remote sensing ; Geography ; Wireless Analytical Chemistry and Chromatography ; Mass Spectrometry Techniques and Applications ; Biosensors and Analytical Detection
Authors: Yaohui Dai | Nian X. Sun | Dexing Jiang | Zhaofeng Wang | Qun Ding | Zhuo Zhang | Yang Liu
Source: International Journal of Biological Macromolecules (article, 2024-08-14)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Ultrathin and highly flexible layered silver nanowires/carboxymethyl cellulose nanofiber nanocomposite films for electromagnetic interference shielding Nanocomposite ; Carboxymethyl cellulose ; Electromagnetic shielding ; Materials science ; Nanofiber ; Nanowire ; Cellulose ; Electromagnetic interference ; Composite material ; Nanotechnology ; Chemical engineering ; Sodium ; Telecommunications ; Metallurgy Electromagnetic wave absorption materials ; Advanced Antenna and Metasurface Technologies ; Dielectric materials and actuators
Authors: Guangyi Zhang | Shusheng Wu | Xichen Xu | Zaihong Wei | Bihe Yuan
Source: Nano-Structures & Nano-Objects (review, 2024-09-09)
Field: Materials Science — Polymers and Plastics

Fabric-based intelligent fire-warning and flame-retardant coating: A review of advances, challenges and prospects Fire retardant ; Coating ; Forensic engineering ; Materials science ; Engineering ; Environmental science ; Composite material Flame retardant materials and properties ; Fire dynamics and safety research ; Polydiacetylene-based materials and applications
Authors: Riikka Haataja | Sami Myllymäki | Vasilii Balanov | Niina Halonen | Tung Phan | Ossi Laitinen | Ping Jack Soh | Heli Jantunen | Henrikki Liimatainen
Source: Materials & Design (article, 2024-05-09)
Field: Materials Science — Polymers and Plastics

Reconfigurable intelligent surface and switchable electromagnetic interference shield based on dynamically adjustable composite film of cellulose nanofibers and VO2 nanoparticles The emerging fields of 5G and 6G telecommunication networks, Internet of Things, and artificial intelligence have intensified the demand for green nanostructured materials with adjustable and intelligent features that respond to external stimuli. By leveraging the insulator-to-metal transition of VO2 nanoparticles, responsive composite films were developed by integrating these nanoparticles within a biopolymeric network of cationic cellulose nanofibers (CNF + ). These films exhibit a reversible change in GHz permittivity upon exposure to thermal or optical stimuli, facilitating dynamic control of their electrical properties. The layered structure of the films further enhances their robustness, featuring a VO2 nanoparticle core encased within CNF + layers. This design not only strengthens the structure but also significantly boosts light-induced conductivity, particularly in layered variant, underscoring its potential in optoelectronic applications. Simulation studies reveal that the nonuniform, reconfigurable intelligent surface (RIS) of the developed mixed film adeptly manipulates incident electromagnetic waves, making it suitable for 5G/6G wireless signals. Conversely, the layered film serves as a switchable electromagnetic interference (EMI) shield, demonstrating notable differences in shielding efficiency between its hot and cold states. Consequently, CNF+/VO2 composite films designed in this work emerge as a versatile, adaptable platform for intelligent electronics, particularly in the realm of 5G/6G wireless communications Materials science ; Nanoparticle ; Composite number ; Optoelectronics ; Electromagnetic interference ; Nanofiber ; Electromagnetic shielding ; Nanotechnology ; Composite material ; Computer science ; Telecommunications Transition Metal Oxide Nanomaterials ; Electromagnetic wave absorption materials
Authors: Ran Wang | Cheng Xu | Xiaotong Zhang
Source: IEEE Transactions on Parallel and Distributed Systems (article, 2024-07-10)
Field: Materials Science — Materials Chemistry

Toward Materials Genome Big-Data: A Blockchain-Based Secure Storage and Efficient Retrieval Method With the advent of the era of data-driven material R&D, more and more countries have begun to build material Big Data sharing platforms to support the design and R&D of new materials. In the application process of material Big Data sharing platforms, storage and retrieval are the basis of resource mining and analysis. However, achieving efficient storage and recovery is not accessible due to the multimodality, isomerization, discrete and other characteristics of material data. At the same time, due to the lack of security mechanisms, how to ensure the integrity and reliability of the original data is also a significant problem faced by researchers. Given these issues, this paper proposes a blockchain-based secure storage and efficient retrieval scheme. Introducing the Improved Merkle Tree (MMT) structure into the block, the transaction data on the chain and the original data in the off-chain cloud are mapped through the material data template. Experimental results show that our proposed MMT structure has no significant impact on the block creation efficiency while improving the retrieval efficiency. At the same time, MMT is superior to state-of-the-art retrieval methods in terms of efficiency, especially regarding range retrieval. The method proposed in this paper is more suitable for the application needs of the material Big Data sharing platform, and the retrieval efficiency has also been significantly improved. Blockchain ; Computer science ; Big data ; Computer data storage ; Distributed database ; Distributed computing ; Data mining ; Computer security ; Operating system Machine Learning in Materials Science ; Recycling and Waste Management Techniques
Authors: Ran Wang | Cheng Xu | Fangwen Ye | S.H. Tang | Xiaotong Zhang
Source: IEEE Internet of Things Journal (article, 2024-01-19)
Field: Materials Science — Materials Chemistry

S-MBDA: A Blockchain-Based Architecture for Secure Storage and Sharing of Material Big Data Material data forms the foundation of the Industrial Internet of Things (IIoT). The rapid advancement of big data technology has opened up new opportunities for material research and development, ushering in the era of data-driven paradigms. As the cornerstone for material genetic engineering technology, the material big data platform is expanding its data scale and facing an increasing demand for sharing in light of the continuous progress and widespread application of big data technology. However, this development also poses security challenges, including the risks of data leakage and tampering. To address these challenges, this article focuses on the National Materials Genetic Engineering Discrete Data Exchange Platform (MGED). It leverages blockchain technology to design a secure material big-data storage and sharing architecture, S-MBDA, ensuring the security and reliability of the material’s big data platform. Additionally, a verifiable retrieval scheme based on a two-layer index structure of bitmap and MPT tree is proposed to enhance the efficiency of blockchain-based retrieval. This scheme aims to guarantee the integrity of retrieval data while achieving efficient and accurate searches across heterogeneous data sources. Through integrating blockchain technology and adopting a novel retrieval scheme, the article presents a comprehensive approach to secure material data storage, sharing, and retrieval. The proposed architecture and scheme address the critical security concerns associated with material big data platforms and contribute to the efficient and accurate retrieval of heterogeneous data. Blockchain ; Computer science ; Big data ; Data security ; Data sharing ; Computer data storage ; Data integrity ; Architecture ; Data retrieval ; Distributed data store ; Data science ; Distributed computing ; Database ; Computer security ; Encryption ; Data mining ; Operating system Machine Learning in Materials Science ; Recycling and Waste Management Techniques ; Blockchain Technology Applications and Security
Authors: Yi Zhao | Wenjing Zhang | Jie Hong | Lei Yang | Yuanyuan Wang | Feng Qu | Wei Xu
Source: The Analyst (article, 2024-01-01)
Field: Chemistry — Spectroscopy

Mobility capillary electrophoresis–native mass spectrometry reveals the dynamic conformational equilibrium of calmodulin and its complexes Benefitting from the rapid evolution of artificial intelligence and structural biology, an expanding collection of high-resolution protein structures has greatly improved our understanding of protein functions. Yet, proteins are inherently flexible, and these static structures can only offer limited snapshots of their true dynamic nature. The conformational and functional changes of calmodulin (CaM) induced by Ca<sup>2+</sup> binding have always been a focus of research. In this study, the conformational dynamics of CaM and its complexes were investigated using a mobility capillary electrophoresis (MCE) and native mass spectrometry (native MS) based method. By analyzing the ellipsoidal geometries of CaM in the solution phase at different Ca<sup>2+</sup> concentrations, it is interesting to discover that CaM molecules, whether bound to Ca<sup>2+</sup> or not, possess both closed and open conformations. Moreover, each individual CaM molecule actively "jumps" (equilibrium exchange) between these two distinct conformations on a timescale ranging from milli- to micro-seconds. The binding of Ca<sup>2+</sup> ions did not affect the structural dynamics of CaM, while the binding of a peptide ligand would stabilize CaM, leading to the observation of a single, compact conformation of the resulting protein complex. A target recognition mechanism was also proposed based on these new findings, suggesting that CaM's interaction with targets may favor a conformational selection model. This enriches our understanding of the binding principles between CaM and its numerous targets. Calmodulin ; Capillary electrophoresis ; Mass spectrometry ; Chemistry ; High resolution ; Resolution (logic) ; Electrophoresis ; Biophysics ; Chromatography ; Biochemistry ; Biology ; Computer science ; Artificial intelligence Mass Spectrometry Techniques and Applications ; Protein Structure and Dynamics ; Metabolomics and Mass Spectrometry Studies
Authors: Binbin Qian | Ke Xu | Dantong Zhang | Sridhar Komarneni | Dongfeng Xue
Source: Fundamental Research (review, 2024-01-03)
Field: Materials Science — Materials Chemistry

A multiscale perspective on cluster-based layered materials: Design and function optimization toward catalytic and energy storage applications Layered materials have become one of the focuses in chemistry and materials science due to their unique structural characteristics and highly tunable properties. Clusters with mesoscale dynamic structures can be introduced into layered materials as structural units or guests, endowing cluster-based layered materials with enhanced or emerging new properties different from those of the individual components. This review starts with a summary of the design and assembly strategies of typical cluster-based layered materials from a multiscale perspective, and then introduces the multiscale characterization and simulation platform. Additionally, we discuss the recent progress in the catalytic and energy storage application of cluster-based layered materials and summarize the current challenges and future directions in this field. Cluster (spacecraft) ; Perspective (graphical) ; Nanotechnology ; Characterization (materials science) ; Field (mathematics) ; Computer science ; Function (biology) ; Energy storage ; Mesoscale meteorology ; Materials science ; Material Design ; Physics Nanocluster Synthesis and Applications ; Inorganic Chemistry and Materials ; Catalytic Processes in Materials Science