jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Z Lei | Cui‐Zhong Guan | Tao Liu | Haibo Huo | Fang Wang | Guoquan Yao
Source: Fuel Cells (article, 2024-10-27)
Field: Materials Science — Materials Chemistry

Voltage and Fuel Utilization Control Strategy for Solid Oxide Fuel Cell Based on Active Disturbance Rejection Control ABSTRACT Solid oxide fuel cell (SOFC) systems have become a research focus because of their clean and high‐efficiency properties. Control of output voltage and fuel utilization is critical to the energy management for multi‐energy systems incorporating SOFC energy supply. However, maintaining precise control of the system's voltage in the presence of perturbations can be challenging. Moreover, the system's voltage control process can lead to fuel utilization fluctuations, which may affect the economy and safety. The design of the controller must meet both of these requirements. The stringent control requirements lead to poor parameter adaptability of existing controllers. This paper designs a nonlinear function and adopts a nonlinear/linear active disturbance rejection controller (ADRC) based on state switching to solve the output voltage tracking control problem of SOFC and maintain the fuel utilization rate in the ideal range. The simulation experimental results show that the proposed method has the advantages of strong and superior parameter adaptability with less control effort, which provides theoretical guidance for the design of the output voltage controller of the actual SOFC system. Disturbance (geology) ; Solid oxide fuel cell ; Fuel cells ; Oxide ; Active disturbance rejection control ; Control (management) ; Control theory (sociology) ; Environmental science ; Materials science ; Chemistry ; Chemical engineering ; Computer science ; Anode ; Engineering ; Electrode ; Physics ; Biology Advancements in Solid Oxide Fuel Cells ; Fuel Cells and Related Materials ; Electrocatalysts for Energy Conversion
Authors: Rui Vitorino
Source: PROTEOMICS - CLINICAL APPLICATIONS (review, 2024-11-28)
Field: Chemistry — Spectroscopy

Minimally Invasive Versus Invasive Proteomics: Urine and Blood Biomarkers in Coronary Artery Disease Coronary artery disease (CAD) is a major cause of morbidity and mortality worldwide. This underlines the urgent need for effective biomarkers for early diagnosis, risk stratification, and therapeutic counseling. Proteomic signatures from plasma and urine have emerged as promising tools for these efforts, each offering unique advantages and challenges. This review provides a detailed comparison of urine and blood proteomic analyzes in the context of CAD and explores their respective advantages and limitations. Urine proteomics offers a minimally invasive, easily repeatable, and temporally stable sampling method, but faces challenges such as lower protein concentrations and potential contamination. Despite its invasive nature, blood proteomics captures high protein concentration and directly reflects systemic physiological changes, making it valuable for acute assessments. Advances in artificial intelligence (AI) have significantly improved the analysis and interpretation of proteomic data, enabling greater accuracy in diagnosis and predictive modeling. AI algorithms, particularly in pattern recognition and data integration, are helping to uncover subtle relationships between biomarkers and disease progression and supporting the discovery of plasma- and urine-based CAD biomarkers. This review demonstrates the potential of combining urine and blood proteomic data using AI to advance personalized approaches in CAD diagnosis and treatment. Future research should focus on standardization of collection protocols, validation of biomarkers in different populations, and the complexity of integrating data from different sources to maximize the potential of proteomics in the treatment of CAD. Proteomics ; Context (archaeology) ; Biomarker discovery ; Disease ; Coronary artery disease ; Medicine ; Bioinformatics ; Data science ; Computer science ; Internal medicine ; Biology Advanced Proteomics Techniques and Applications ; Metabolomics and Mass Spectrometry Studies ; Advanced Biosensing Techniques and Applications
Authors: Mohammad Zaki | Carsten Prinz | Bastian Ruehle
Source: ChemRxiv (preprint, 2024-12-08)
Field: Materials Science — Materials Chemistry

A Self-Driving Lab for Nano- and Advanced Materials Synthesis The recent emergence of self-driving labs (SDL) and material acceleration platforms (MAPs) demonstrates the ability of these systems to change the way chemistry and material syntheses will be performed in the future. Especially in conjunction with nano and advanced materials which are generally recognized for their great potential in solving current material science challenges, such systems can make disrupting contributions. Here, we describe in detail MINERVA - an SDL specifically built and designed for the synthesis, purification, and in line characterization of nano and advanced materials. By fully automating these three process steps for seven different materials from five representative, completely different classes of nano and advanced materials (metal, metal oxide, silica, metal organic framework, and core-shell particles) that follow different reaction mechanisms, we demonstrate the great versatility and flexibility of the platform. We further study the reproducibility and particle size distributions of these seven representative materials in depth and show the excellent performance of the platform when synthesizing these material classes. Lastly, we discuss the design considerations as well as the hardware and software components that went into building the platform and make all components publicly available. Flexibility (engineering) ; Characterization (materials science) ; Nano- ; Nanotechnology ; Computer science ; Process (computing) ; Software ; Materials science ; Operating system Electronic and Structural Properties of Oxides ; Quantum Dots Synthesis And Properties ; Nanocluster Synthesis and Applications
Authors: Jackie T. Yik | Carl Hvarfner | Jens Sjölund | Erik J. Berg | Leiting Zhang
Source: ChemRxiv (preprint, 2024-12-12)
Field: Materials Science — Materials Chemistry

Towards Self-Driving Labs for Better Batteries: Accelerating Electrolyte Discovery with Automation and Bayesian Optimization The integration of automation and data-driven methodologies offer a promising approach to accelerating materials discovery in energy storage research. Thus far, in battery research, coin-cell assembly has advanced to become near fully-automated but remains largely disconnected from data-driven methods, which have been primarily developed for computational or multi-fidelity datasets. To bridge the disconnect, this work presents a self-driving laboratory framework designed to accelerate electrolyte discovery by integrating automated coin-cell assembly, galvanostatic cycling of LiFePO4||Li4Ti5O12 organic-aqueous full-cells, and Bayesian optimization for selecting subsequent experiments based on prior results. The integration of Bayesian optimization highlights machine-intelligent decision-making, enabling closed-loop experimentation-analysis workflow. The study focuses on an organic-aqueous hybrid electrolyte system comprising four co-solvents—dimethyl sulfoxide, trimethyl phosphate, acetonitrile, and water—and two salts, lithium perchlorate and lithium bis(trifluoromethanesulfonyl)imide (LiTFSI). Using this framework, electrolyte formulations with at least 94% Coulombic efficiency were identified. Additionally, quantification of hydrogen evolution by online electrochemical mass spectrometry revealed a direct correlation between the electrolyte water content and the hydrogen evolution kinetics, irrespective of the electrolyte co-solvent compositions. The results highlight the potential of combining Bayesian optimization with autonomous experimentation, while contributing new insights into electrolyte design for next-generation sustainable aqueous batteries. Electrolyte ; Computer science ; Bayesian optimization ; Battery (electricity) ; Workflow ; Lithium (medication) ; Process engineering ; Materials science ; Chemistry ; Electrode ; Engineering ; Machine learning Machine Learning in Materials Science ; Advanced Battery Technologies Research ; Fuel Cells and Related Materials
Authors: Andrew Larsen | Ross Lee | Caden Wilson | John D. Hedengren | John R. Benson | Matthew Memmott
Source: Nuclear Engineering and Design (article, 2024-06-12)
Field: Materials Science — Materials Chemistry

Multi-objective optimization of molten salt microreactor shielding perturbations employing machine learning Microreactor ; Electromagnetic shielding ; Molten salt ; Nuclear engineering ; Materials science ; Salt (chemistry) ; Mechanical engineering ; Engineering ; Process engineering ; Waste management ; Metallurgy ; Composite material ; Chemistry ; Catalysis Graphite, nuclear technology, radiation studies ; Nuclear reactor physics and engineering ; Radiation Effects in Electronics
Authors: Hiroaki Okada | Satoshi Maeda
Source: ACS Omega (article, 2024-01-29)
Field: Materials Science — Materials Chemistry

On Accelerating Substrate Optimization Using Computational Gibbs Energy Barriers: A Numerical Consideration Utilizing a Computational Data Set Substrate optimization is a time- and resource-consuming step in organic synthesis. Recent advances in chemo- and materials-informatics provide systematic and efficient procedures utilizing tools such as Bayesian optimization (BO). This study explores the possibility of reducing the required experiments further by utilizing computational Gibbs energy barriers. To thoroughly validate the impact of using computational Gibbs energy barriers in BO-assisted substrate optimization, this study employs a computational Gibbs energy barrier data set in the literature and performs an extensive numerical investigation virtually regarding the Gibbs energy barriers as virtual experimental results and those with systematic and random noises as virtual computational results. The present numerical investigation shows that even the computational reactivity affected by noises of as much as 20 kJ/mol helps reduce the number of required experiments. Gibbs free energy ; Computer science ; Gibbs sampling ; Set (abstract data type) ; Bayesian optimization ; Mathematical optimization ; Bayesian probability ; Artificial intelligence ; Mathematics ; Thermodynamics ; Physics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Process Optimization and Integration
Authors: Nathan S. Johnson | Aashwin Mishra | Dylan J. Kirsch | Apurva Mehta
Source: Materials (article, 2024-08-14)
Field: Materials Science — Materials Chemistry

Active Learning for Rapid Targeted Synthesis of Compositionally Complex Alloys The next generation of advanced materials is tending toward increasingly complex compositions. Synthesizing precise composition is time-consuming and becomes exponentially demanding with increasing compositional complexity. An experienced human operator does significantly better than a novice but still struggles to consistently achieve precision when synthesis parameters are coupled. The time to optimize synthesis becomes a barrier to exploring scientifically and technologically exciting compositionally complex materials. This investigation demonstrates an active learning (AL) approach for optimizing physical vapor deposition synthesis of thin-film alloys with up to five principal elements. We compared AL-based on Gaussian process (GP) and random forest (RF) models. The best performing models were able to discover synthesis parameters for a target quinary alloy in 14 iterations. We also demonstrate the capability of these models to be used in transfer learning tasks. RF and GP models trained on lower dimensional systems (i.e., ternary, quarternary) show an immediate improvement in prediction accuracy compared to models trained only on quinary samples. Furthermore, samples that only share a few elements in common with the target composition can be used for model pre-training. We believe that such AL approaches can be widely adapted to significantly accelerate the exploration of compositionally complex materials. Quinary ; Ternary operation ; Computer science ; Process (computing) ; Random forest ; Artificial intelligence ; Complex system ; Materials science ; Algorithm ; Machine learning ; Alloy ; Metallurgy Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; Advanced Materials Characterization Techniques
Authors: Lorenz J. Falling
Source: ACS Physical Chemistry Au (review, 2024-06-12)
Field: Materials Science — Materials Chemistry

A Vision for the Future of Materials Innovation and How to Fast-Track It with Services Today, we witness how our scientific ecosystem tries to accommodate a new form of intelligence, artificial intelligence (AI). To make the most of AI in materials science, we need to make the data from computational and laboratory experiments machine-readable, but while that works well for computational experiments, integrating laboratory hardware into a digital workflow seems to be a formidable barrier toward that goal. This paper explores measurement services as a way to lower this barrier. I envision the Entity for Multivariate Material Analysis (EMMA), a centralized service that offers measurement bundles tailored for common research needs. EMMA's true strength, however, lies in its software ecosystem to treat, simulate, and store the measured data. Its close integration of measurements and their simulation not only produces metadata-rich experimental data but also provides a self-consistent framework that links the sample with a snapshot of its digital twin. If EMMA was to materialize, its database of experimental data connected to digital twins could serve as the fuel for physics-informed machine learning and a trustworthy horizon of expectations for material properties. This drives material innovation since knowing the statistics helps find the exceptional. This is the EMMA approach: fast-tracking material innovation by integrated measurement and software services. Metadata ; Workflow ; Computer science ; Software ; Data science ; Trustworthiness ; Scientific instrument ; Artificial intelligence ; Database ; World Wide Web ; Computer security Machine Learning in Materials Science ; Research Data Management Practices ; Scientific Computing and Data Management
Authors: Kaidong Song | A. N. M. Tanvir | Md. Omarsany Bappy | Yanliang Zhang
Source: arXiv (Cornell University) (preprint, 2024-03-09)
Field: Materials Science — Materials Chemistry

New Directions for Thermoelectrics: A Roadmap from High-Throughput Materials Discovery to Advanced Device Manufacturing Thermoelectric materials, which can convert waste heat into electricity or act as solid-state Peltier coolers, are emerging as key technologies to address global energy shortages and environmental sustainability. However, discovering materials with high thermoelectric conversion efficiency is a complex and slow process. The emerging field of high-throughput material discovery demonstrates its potential to accelerate the development of new thermoelectric materials combining high efficiency and low cost. The synergistic integration of high-throughput material processing and characterization techniques with machine learning algorithms can form an efficient closed-loop process to generate and analyze broad data sets to discover new thermoelectric materials with unprecedented performances. Meanwhile, the recent development of advanced manufacturing methods provides exciting opportunities to realize scalable, low-cost, and energy-efficient fabrication of thermoelectric devices. This review provides an overview of recent advances in discovering thermoelectric materials using high-throughput methods, including processing, characterization, and screening. Advanced manufacturing methods of thermoelectric devices are also introduced to realize the broad impacts of thermoelectric materials in power generation and solid-state cooling. In the end, this paper also discusses the future research prospects and directions. Throughput ; Thermoelectric materials ; Manufacturing engineering ; Nanotechnology ; Engineering ; Computer science ; Materials science ; Electrical engineering ; Operating system Machine Learning in Materials Science ; Advanced Thermoelectric Materials and Devices
Authors: Jack Porter | Jacob Roberts | Gavin J. Miller
Source: The Journal of Organic Chemistry (article, 2024-09-12)
Field: Chemistry — Organic Chemistry

Benzoylation of Tetrols: A Comparison of Regioselectivity Patterns for <i>O-</i> and <i>S-</i>Glycosides of <scp>d</scp>-Galactose Efficient protecting group strategies are important for glycan synthesis and represent a unique synthetic challenge in differentiating sugar ring hydroxyl groups. Direct methods to enable regioselective protecting group installation are thus desirable. Herein, we explore a one-step regioselective benzoylation to deliver 2,3,6-protected d-galactose building blocks from tetrols across a variety of α- and β-, <i>O</i>- and <i>S</i>-glycoside substrates. We focus on benzoyl chloride as the esterifying reagent and a reaction temperature of -40 °C to screen the regioselectivity outcome for twenty-two different glycosides, based on isolated yields. Using this methodology, we demonstrate the capability for α-linked aryl and alkyl glycosides (<i>O</i>- and <i>S</i>- d-galactosides, d-galactosamines, and l-fucose), delivering consistent isolated yields (>65%) for 2,3,6-benzoylated products. We extend to explore β-linked systems, where the observed regioselectivity is not paralleled. We posit that both steric and electronic factors from the anomeric substituent contribute to modulating the reactivity competition between 2-OH and 4-OH, enabling the formation of regioisomeric mixtures. However, a certain balance of these factors within the aglycon can deliver 2,3,6-regioselectivity, notably for <i>β-O</i>-Et and <i>β-O</i>-CH<sub>2</sub>CF<sub>3</sub> glycosides. The methodology contributes toward understanding the peculiarities of regioselective carbohydrate-protecting group installation, exploring the importance of the anomeric substituent upon ring hydroxyl group reactivity. Regioselectivity ; Glycoside ; Chemistry ; Galactose ; Stereochemistry ; Organic chemistry ; Catalysis Carbohydrate Chemistry and Synthesis ; Enzyme Catalysis and Immobilization ; Amino Acid Enzymes and Metabolism
Authors: Jin Da Tan | Andre K. Y. Low | Shannon RY Thoi | Sze Yu Tan | Wenguang Zhao | Yee‐Fun Lim | Qianxiao Li | Saif A. Khan | Balamurugan Ramalingam | Kedar Hippalgaonkar
Source: ChemRxiv (preprint, 2024-07-02)
Field: Chemistry — Organic Chemistry

Multi-Objective Synthesis Optimization and Kinetics of a Sustainable Terpolymer The properties of polymers are primarily influenced by their monomer constituents, functional groups, and their mode of linkages. Copolymers, synthesized from multiple monomers, offer unique material properties compared to their homopolymers. Optimizing the synthesis of terpolymers is a complex and labor-intensive task due to variations in monomer reactivity and their compositional shifts throughout the polymerization process. The present work focuses on polymerization of styrene, myrcene, and dibutyl itaconate (DBI) with the goal of achieving a high glass transition temperature (T_g) in the resulting terpolymer. While the copolymerization of pairwise combinations of styrene, myrcene, and DBI have been previously investigated, the terpolymerization of all three at once remains unexplored. Terpolymers with monomers like styrene would provide high glass transition temperatures as the resultant polymers exhibit a rigid glassy state at ambient temperatures. Conversely, minimizing styrene incorporation also reduces reliance on petrochemical-derived monomer sources for terpolymer synthesis, thus enhancing the sustainability of terpolymer usage. To balance the objectives of maximizing T_g while minimizing styrene incorporation, we employ multi-objective Bayesian Optimization to efficiently sample in a design space comprising 5 experimental parameters. We perform two iterations of optimization for a total of 89 terpolymers, reporting terpolymers with a T_g above ambient temperature while retaining less than 50% styrene incorporation. This underscores the potential for exploring and utilizing renewable monomers such as myrcene and DBI, to foster sustainability in polymer synthesis. Additionally, the dataset enables the calculation of ternary reactivity ratios using a system of Ordinary Differential Equations based on the Terminal Model, providing valuable insights into the reactivity of monomers in complex ternary systems compared to binary copolymer systems. This approach reveals the nuanced kinetics of terpolymerization, further informing the synthesis of polymers with desired properties. Copolymer ; Styrene ; Monomer ; Materials science ; Polymer ; Reactivity (psychology) ; Polymerization ; Polymer chemistry ; Chemical engineering ; Composite material Advanced Polymer Synthesis and Characterization ; Machine Learning in Materials Science ; Chemistry and Chemical Engineering
Authors: Simon Matoori
Source: ACS Materials Au (review, 2024-12-23)
Field: Materials Science — Biomaterials

Hallmarks of Polymersome Characterization Polymersomes have the potential to become the next generation of vesicular drug delivery systems. Their high chemical versatility and in certain cases higher membrane stability than liposomes raised the high hopes for polymersomes as a drug carrier, but the clinical translation has been slow. To jump-start translation, there is a need for meticulous characterization and reporting of key parameters of polymersome formulations. Regulatory authorities have provided valuable insights on critical quality attributes of liposomes in their guidance document on liposomal nanosimilars. Inspired by this guidance document, this Perspective proposes necessary characterization of polymersomes (hallmarks) regarding their chemical composition, physicochemical properties, drug release profile, stability, stimuli responsiveness, and pharmacokinetics and biodistribution. Polymersome ; Biodistribution ; Liposome ; Drug delivery ; Characterization (materials science) ; Nanotechnology ; Vesicle ; Computer science ; Chemistry ; Materials science ; Membrane ; Biochemistry Nanoparticle-Based Drug Delivery ; Advanced Polymer Synthesis and Characterization ; RNA Interference and Gene Delivery