jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Austin M. Mroz | Benjamin D. Egleston | James Sherwood | Ruby Morel | Kim E. Jelfs | Rebecca L. Greenaway
Source: ChemRxiv (preprint, 2025-02-27)
Field: Materials Science — Materials Chemistry

Predicting Solubility and Size-Exclusivity Towards the Rational Design of Porous Liquid Solutions Porous liquids are a sub-class of porous materials that combine permanent porosity, typically associated with solids, with the fluidity and fast mass-transfer capabilities of liquids, making them ideal candidates for gas storage and separation applications. One strategy to form porous liquids is the dissolution of discrete and permanently porous molecular species at relatively high concentrations in cavity-excluded solvents, thus introducing permanent porosity into the liquid in which it is dissolved and ensuring a solution of reasonable porosity is obtained. To access high-performance porous liquids for target applications, the selection of both the porous molecular species and the cavity-excluded solvent is key to ensuring the solvent is permanently excluded and the pore carrier is highly soluble. Finding new solvents that fit both these requirements is challenging, often resulting in a trial-and-error approach. While predictive data-driven models may be attractive, the youth of the porous liquid field currently limits the availability of data necessary to train robust models. Here, we present a computational workflow for the discovery of new porous liquid solutions combining solubility prediction software and a size-exclusivity prediction algorithm, featuring no incorrect size-exclusivity predictions; this is followed by experimental validation with a representative system. Our workflow yielded size-excluded solvent and soluble porous organic cage pairs, leading to the realisation of a new porous liquid with enhanced methane uptake compared to previous systems discovered in a purely experimental high-throughput brute-force manner, highlighting the advantages of incorporating a computational workflow in the discovery of new porous liquids. Solubility ; Porosity ; Rational design ; Order (exchange) ; Chemical engineering ; Chemistry ; Materials science ; Process engineering ; Nanotechnology ; Business ; Organic chemistry ; Engineering Crystallization and Solubility Studies ; Process Optimization and Integration
Authors: Rong Nie | Ling Wu | Lijun Zhou | Yuejun Li
Source:  (article, 2025-10-14)
Field: Materials Science — Materials Chemistry

Environmental Degradation Prediction of Coating Materials Based on FT-Transformer Environmental degradation significantly affects coating materials' service life, making accurate prediction under complex conditions crucial for material design and maintenance. This study applies the Feature Token Transformer (FTTransformer) to predict coating degradation using a dataset of 32 experimental units from NIST SPHERE accelerated aging tests under different temperature-humidity combinations. The FTTransformer's specialized feature tokenization mechanism uniformly handles heterogeneous tabular data containing numerical features (time, log-time) and categorical features (temperature, humidity, unit ID), distinguishing it from standard Transformer architectures designed for sequential data. Through rigorous group-wise data splitting that ensures test units are completely unseen during training, the model achieves exceptional performance with <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$R^{2}=0.965$</tex> and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{R M S E}=\mathbf{0. 0 1 2 7}$</tex> on the original scale, significantly outperforming traditional methods and vanilla Transformer. The model successfully captures physically consistent degradation patterns including monotonic temperature acceleration following Arrhenius-like behavior and nonmonotonic humidity effects where mid-range conditions (25-50% RH) produce peak degradation while extreme conditions show reduced impact. These findings align with competing degradation mechanisms in polymeric coatings and demonstrate the model's capability for data-driven lifetime assessment and predictive maintenance applications. Degradation (telecommunications) ; NIST ; Coating ; Categorical variable ; Transformer ; Feature (linguistics) ; Test data ; Environmental tests Machine Learning in Materials Science ; Polymer crystallization and properties ; Epoxy Resin Curing Processes
Authors: Pedro
G. C. Lucena (22528421) | Flávia L. R. Lessa (22528424) | Jhonattas C. Carregosa (17130282) | Marcos N. Eberlin (745128) | Alberto Wisniewski Jr (22528427) | Jandyson M. Santos (3820153)
Source: Figshare (article, 2025-10-30)
Field: Chemistry — Analytical Chemistry

MolecularPatterns of Crude Oils with Varying NaphthenicAcidity Revealed by ESI(−)-FT-ICR MS and Unsupervised Learning Acidic crude oils are becoming more prominent in global markets due to technological advances that enable the exploitation of unconventional reserves. Understanding their chemical nature is essential for refining strategies, especially as acidity poses processing challenges. Unsupervised pattern recognition offers a way to uncover meaningful relationships between molecular composition and macroscopic acidity, reducing the manual interpretation effort. This study proposes an unsupervised learning framework to reveal such patterns efficiently. Crude oil samples from the Sergipe-Alagoas Basin (northeast Brazil) were analyzed using negative electrospray ionization Fourier transform ion cyclotron resonance mass spectrometry (ESI(−)-FT-ICR MS), focusing on polar compounds. Bulk acidity was assessed through total acid number (TAN), naphthenic acidity index (NAI), and percentage of naphthenic acids (%NA). K-means clustering applied to these parameters identified four acidity-level groups. Clusters I and II contained low-TAN oils (<1 mg KOH g<sup>–1</sup>), differing in %NA, while Clusters III and IV grouped high-TAN oils (>1 mg KOH g<sup>–1</sup>), also separated by %NA. Multidimensional scaling (MDS) was applied to the autoscaled abundance of globally shared molecular formulas from the O<sub>1</sub>–O<sub>4</sub> oxygen classes. While individual classes had limited discriminatory power, combined O<sub>1</sub>–O<sub>3</sub> formulas enabled clear differentiation among acidity levels. This framework demonstrates that the data-driven analysis of high-resolution mass spectrometry data can be automated to enhance the molecular understanding of acidic oils, providing valuable insights for refining decisions and linking molecular fingerprints to macroscopic acidity behavior. Naphthenic acid ; Refining (metallurgy) ; Electrospray ionization ; Cluster analysis ; Fourier transform ion cyclotron resonance ; Mass spectrometry ; Compositional data ; Unsupervised learning Petroleum Processing and Analysis ; Hydrocarbon exploration and reservoir analysis ; Lubricants and Their Additives
Source: Journal of Physics Conference Series (article, 2025-05-01)
Field: Materials Science — Materials Chemistry

Preface It is with great pleasure that we present the proceedings of the 2025 6 th International Conference on Advanced Material and Clean Energy (ICAMCE 2025), held in Zhengzhou, China, from February 21 to 23, 2025. This conference, hosted by Huanghe University of Science and Technology, brought together experts, researchers, and scholars from around the globe to explore the latest advancements and innovations in the fields of advanced materials and clean energy technologies. ICAMCE 2025 aimed to foster international collaboration and provide a platform for the exchange of ideas, knowledge, and research findings that can drive sustainable development and address global energy challenges. The conference featured a diverse and enriching program, highlighted by seven keynote speeches delivered by distinguished professors from renowned institutions. Prof. Yuping Wu from Southeast University, China, opened the event with an insightful discussion on tailoring the transport of electrons and ions in electrochemical energy storage systems. Prof. Chaogang Niu from Zhengzhou University presented groundbreaking research on high-energy and low-expansion lithium metal pouch cells, while Prof. Xinyong Li from Dalian University of Technology shared his expertise on the design of spinels-derived functional nanocomposites for green catalysis and sustainable energy utilization. Prof. Shouren Zhang from Huanghe University of Science and Technology explored the modulation of optical properties in nanocomposites and their applications, and Prof. Lei Zhang from Nanjing University of Information Science &amp; Technology introduced data-driven language models for materials science. Additionally, Prof. Haihong Wu from Huanghe University of Science and Technology discussed the preparation and application of carbon fiber cells, and Prof. Kok-Keong Chong from Universiti Tunku Abdul Rahman, Malaysia, presented his work on dense-array concentrator photovoltaic systems. These keynote speeches set the tone for the conference, inspiring attendees with cutting-edge research and innovative approaches. ICAMCE 2025 also provided a platform for researchers to present their work through paper presentations. From a large number of submissions, the best ones of all papers were carefully selected for inclusion in the conference proceedings. These papers reflect the high quality and diversity of research in the fields of advanced materials and clean energy, covering topics such as nanomaterials, energy storage, renewable energy systems, and sustainable technologies. The selection process was rigorous, ensuring that only the most impactful and innovative contributions were included. List of Committee member are available in this PDF. Philosophy Machine Learning in Materials Science ; Advancements in Battery Materials ; Electrocatalysts for Energy Conversion
Authors: Evgeny Khoroshilov
Source: США &amp Канада экономика – политика – культура / USA &amp Canada Economics – Politics – Culture (article, 2025-01-01)
Field: Chemistry — Inorganic Chemistry

Canada&amp;apos;s Role in Global Uranium Market Over the past few years, the situation in the global nuclear energy industry has been described by the term &amp;quot;atomic renaissance&amp;quot;. Nuclear energy is acquiring a special role in connection with the processes of digitalization and development of artificial intelligence, which are behind the growing demand for electricity and requirements for uninterrupted power supply. At the same time, for the United States and its satellites, the development of nuclear energy is not only an integral part of a large–scale technological race, but also a tool for ensuring energy security. It is fundamentally important for the collective West that its nuclear generation in the future can be fully provided with its own raw material base of uranium, primarily of Canadian and Australian origin. Canada ranks 2nd in the world in terms of uranium production and 3rd in terms of uranium reserves. In addition, a distinctive feature of a number of large Canadian uranium deposits are unique ores with the highest uranium content. Currently, Canada is declaring its ambitions to seize in the medium-term global leadership in the uranium market and become the privileged supplier of this raw material to the United States, EU member states, Japan and South Korea. In addition, Canada is interested in the absence of competition from Russia in the global uranium market and intends to make efforts not to cede its market share to the Russian side. At the same time, Ottawa is trying to restrict China&amp;apos;s access to Canadian uranium resources by pursuing a policy of discrimination against Chinese uranium buyers and mining companies. Uranium ; Nuclear power ; Competition (biology) ; Energy market ; Enriched uranium ; Electricity ; Production (economics) ; Energy policy ; State (computer science) Radioactive element chemistry and processing ; Global Energy Security and Policy ; International Environmental Law and Policies
Authors: Sihan Wang | Xin Liu | Yong Juan | Junyang Li | Tingyuan Zhang | Wei Wang
Source: Nano Research (article, 2025-12-08)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Construction of mutifunctional Cu sphere@ZIF-derived Co particle/carbon fiber composites for high-efficiency microwave absorption Currently, the development of artificial intelligence and new-generation high-frequency communication technologies has placed increasingly higher demands on the performance of electromagnetic wave (EMW) absorbing materials with multiple functions in complex application scenarios. In this work, using Cu sphere@ZIF-67 composites as the precursor, the Cu sphere@ZIF-derived Co particles/carbon fiber flexible (CCC) composites<strong> </strong>are designed by electrostatic spinning and high-temperature calcination technique. Herein, a specific cross-linked structure is formed with the inset of ZIF-derived Co particles and Cu spheres into the CNF network, allowing effectively tuning the electromagnetic parameters, optimizing the impedance matching and improving the EMW absorption performance of CCC composites. The as-obtained composites gain a high minimum reflection loss (RL<sub>min</sub>) of -78.33 dB and a broad effective absorption bandwidth (EAB) of 7.60 GHz. Apparently, the three-dimensional interlaced complex network structure endows the composites with enhanced thermal conductivity and superhydrophobic properties, making it conducive to heat transfer and inhibiting the absorption of surface water. Meanwhile, the conductivity of the composites is greatly sensitive to the bending deformation, enabling their applications in flexible sensing. Briefly, this work provides a novel and feasible design thought to fabricate carbon-based composites with multifunctional properties of EMW absorption, heat-conducting, self-cleaning and strain-sensitive conductivity, which opens a route for the potential application of EMW absorbing materials under some extreme conditions. Reflection loss ; Microwave ; Thermal conductivity ; Spinning ; Electromagnetic radiation ; Absorption (acoustics) ; Conductivity ; Impedance matching ; Calcination Electromagnetic wave absorption materials ; Metamaterials and Metasurfaces Applications ; Advanced Antenna and Metasurface Technologies
Authors: Shuo Liu (108276) | Yaxi Cheng (22550800) | Wanyu Xiao (17838689) | Yaru Li (3340731) | Yidi Li (10840791) | Jiajin Li (457126) | Lixia Bao (10058355)
Source: Figshare (article, 2025-11-03)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Tuning Oxygen VacancyConcentration in Defect-EngineeredBlack TiO<sub>2</sub> Nanomaterials for Synergistically Enhanced MicrowaveAbsorption Performance With the rapid advancement of 5G communication and artificial intelligence technologies, the ubiquitous deployment of electronic apparatuses has raised pressing issues relating to signal interference and electromagnetic pollution, driving intensive research into microwave-absorbing materials. This article proposed a defect engineering-driven strategy to endow nanostructured TiO<sub>2</sub> with excellent microwave absorption performance through the synergistic optimization of oxygen vacancies and core–shell nanostructure design. By precisely controlling the hydrogenation temperature (300–450 °C) and TiO<sub>2</sub>/NaBH<sub>4</sub> ratio (1–4), we achieved tunable microwave absorption performance. Under optimal conditions, the nanomaterial exhibited a RL<sub>min</sub> of −32.95 dB and an effective absorption bandwidth of 2.794 GHz, showing great promise for electromagnetic interference shielding in next-generation communication devices and radar systems. The concentration of oxygen vacancies was the core factor affecting the absorption performance. Incorporating oxygen vacancies not only enhanced defect-induced polarization and interface polarization effects but also strengthened conductivity loss by increasing carrier concentration. The collaborative interplay of these three mechanisms endows the nanostructured material with a superior microwave attenuation capability. The findings provide theoretical and practical guidance for developing lightweight, broadband, and highly efficient black TiO<sub>2</sub>-derived microwave absorbers. Our findings offer practical guidance for developing advanced microwave absorbers for next-generation aerospace radar and communication systems. Microwave ; Nanomaterials ; Attenuation ; Radar ; Absorption (acoustics) ; Nanoparticle ; Nanostructure ; Polarization (electrochemistry) Electromagnetic wave absorption materials ; Magnetic Properties and Synthesis of Ferrites ; Microwave-Assisted Synthesis and Applications
Authors: Kolter Bradshaw | Ammar Hakim | Bikshandarkoil R. Srinivasan
Source: ArXiv.org (preprint, 2025-01-29)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Effects of oxidation and impurities in lithium surfaces on the emitting wall plasma sheath Use of lithium as a surface coating in fusion devices improves plasma performance, but the change in wall properties affects the secondary electron emission properties of the material. Lithium oxidizes easily, which drives the emission yield well above unity. We present here simulations demonstrating the change in sheath structure from monotonic to the nonmonotonic space-charge limited sheath using an energy-dependent data-driven emission model which self-consistently captures both secondary emission and backscattering populations. Increased secondary electron emission from the material has ramifications for the degradation and erosion of the wall. Results shows that the oxidation leads to an increased electron flux into the wall, and a reduced ion flux. The net transfer of energy to the surface is significantly greater for the oxidized case than for the pure lithium case. High reflection rates of low-energy backscattered particles leads to a high re-emission rate at the wall. Impurity ; Lithium (medication) ; Plasma ; Materials science ; Chemistry ; Physics ; Nuclear physics ; Internal medicine ; Medicine Copper Interconnects and Reliability ; Semiconductor materials and devices
Authors: Zhaolong Wang | Shuo Yang | Mingzhu Xie | Xiaolong Wang | Qihui Xie | Yong Shuai
Source: Advanced Functional Materials (article, 2025-12-04)
Field: Materials Science — Surfaces, Coatings and Films

Bioinspired Janus Microfluidic Heat Sink for Ultra‐Efficient CPU Cooling via Millisecond‐Scale Bubble Transport Abstract Efficient thermal management is increasingly critical for high‐performance computing and emerging artificial intelligence hardware, where heat fluxes exceed the capabilities of conventional heat sinks. Here, a 3D‐printed bioinspired Janus microchannel heat sink is reported that enables ultrafast bubble transport and stable two‐phase cooling for electronic devices. Inspired by the Namib desert beetle, the heat sink integrates asymmetric wettability‐a superhydrophobic top surface and hydrophilic bottom surface‐creating a Laplace pressure‐driven pathway for millisecond‐scale bubble removal. This architecture suppresses vapor‐film formation, maintains continuous liquid replenishment, and achieves a critical heat flux (CHF) of 105 W cm −2 , representing an up to 125% improvement over conventional microchannel heat sinks. Integrated into a commercial CPU, the Janus microfluidic heat sink maintains maximum clock frequency under full load without thermal throttling, demonstrating a scalable material‐driven solution for next‐generation thermal management. This bioinspired approach establishes a platform for programmable surface functionality in high‐power electronics, with potential applications in data centers, electric vehicles, and aerospace systems. Heat sink ; Microfluidics ; Microchannel ; Bubble ; Heat flux ; Janus ; Computer cooling ; Thermal ; Heat transfer Surface Modification and Superhydrophobicity ; Pickering emulsions and particle stabilization ; Innovative Microfluidic and Catalytic Techniques Innovation
Authors: Laura Rodríguez Raurell
Source: Dipòsit Digital de la Universitat de Barcelona (Universitat de Barcelona) (article, 2025-11-20)
Field: Chemistry — Spectroscopy

Lighting the Way: Supramolecular Luminescent Chemosensors for a Safer Environment In this Guest Editorial, the special issue of ChemistryEurope on supramolecular luminescent chemosensors (LCs) is presented, showcasing selected contributions from members of the COST Action LUCES (CA22131). LCs are a versatile means to sensitively and selectively detect pollutants and biologically relevant molecules. Recent advances in this rapidly growing field include molecular receptors and nanostructured assemblies, as well as device integration and data-driven sensing. Articles in this issue highlight a range of innovations within European collaborations fostered by LUCES, demonstrating the Action’s focus on translating fundamental findings from the laboratory into real-life solutions to environmental and societal needs. SAFER ; Supramolecular chemistry ; Action (physics) ; Focus (optics) Molecular Sensors and Ion Detection ; Luminescence and Fluorescent Materials ; Supramolecular Chemistry and Complexes
Authors: Renquan Lv (21259155) | Weiwei Han (286593) | Zixu Zeng (21259158) | Yi He (408132) | Lecheng Lei (1645309) | Ping Li (31981) | Xingwang Zhang (587405)
Source: Figshare (article, 2025-07-24)
Field: Materials Science — Materials Chemistry

Machine Learningfor Controlling the Particle SizeDistribution of TiO<sub><b>2</b></sub> Nanoparticles in OpticalNanocomposites To achieve precise control over the particle size distribution (PSD) of titanium oxide (TiO<sub>2</sub>) nanoparticles (NPs) in optical nanocomposites, this study introduces a GBDT-RR (Gradient Boosted Decision Tree-Rosin-Rammler) framework to guide their synthesis. We first constructed a TiO<sub>2</sub> synthesis-PSD data set based on hydrodynamic diameter by combining Syn-ext Llama with manual collaboration. Based on this data set, we integrated a GBDT model for high-precision prediction with an RR model for concise PSD representation. Moreover, this framework was embedded into a Bayesian optimization (BO) algorithm (GBDT-RR with BO) to develop an efficient synthesis reverse design platform. Experimental validation confirmed the platform’s efficiency, with the computational process to guide the synthesis of 15 nm TiO<sub>2</sub> NPs taking only 5 min. Feature importance analysis offered mechanistic insights into the nucleation process. Additionally, its versatility was proven by successful extension to ZrO<sub>2</sub> and CeO<sub>2</sub> NPs. In summary, the GBDT-RR framework and its associated synthesis reverse design platform represent a powerful, data-driven tool for the precise and efficient optimization of TiO<sub>2</sub> NP PSDs, facilitating their applications in optical nanocomposites. Bayesian optimization ; Nanoparticle ; Process (computing) ; Feature (linguistics) ; Set (abstract data type) ; Particle (ecology) ; Particle size ; Bayesian probability Machine Learning in Materials Science ; TiO2 Photocatalysis and Solar Cells ; Block Copolymer Self-Assembly