jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Piracha Sanwal | Ali Raza | Yu‐Xin Miao | Brock Lumbers | Gao Li
Source: Polyoxometalates (article, 2024-01-25)
Field: Materials Science — Materials Chemistry

Advances in coinage metal nanoclusters: From synthesis strategies to electrocatalytic performance As a result of the powerful quantum confined space effects and multiple modes of very tiny atomic sizes, metal nanoclusters (NCs) that consist of thiolate-protected noble metals, such as silver (Ag) and gold (Au) have core sizes less than 3 nm, have emerged as a class of "metallic molecules" with a wide range of molecule-like optical, magnetic, and electronic properties. To investigate well-defined nanocatalysts, especially ligand-passivated metal nanoclusters, great strides have been achieved in the efficient synthesis of atomically precise nanoparticles. Methods of synthesis such as bottom-up growth, top-down approach, ligand engineering, and the interconversion system are covered in this overview. Such clearly defined metal nanoclusters have demonstrated considerable promise in catalysis research and have evolved into a distinct class of model catalysts. Focusing on the oxygen reduction reaction (ORR), hydrogen evolution reaction (HER), and oxygen evolution reaction (OER), this article attempts to outline current developments in nanoclusters of molecular metal employed in electrocatalytic reactions. The paper also highlights the connection between the catalytic mechanism's structure and performance as well as examines the potential effects of metal clusters' sizes, metal core structures, charges, ligands, and metal-ligand binding patterns on their electrocatalytic activity. Future research opportunities and challenges are also proposed. Nanoclusters ; Nanomaterial-based catalyst ; Nanotechnology ; Catalysis ; Metal ; Ligand (biochemistry) ; Materials science ; Noble metal ; Electrocatalyst ; Oxygen evolution ; Molecule ; Chemistry ; Combinatorial chemistry ; Nanoparticle ; Electrochemistry ; Physical chemistry ; Metallurgy ; Organic chemistry ; Electrode Nanocluster Synthesis and Applications ; Advanced Nanomaterials in Catalysis ; Gold and Silver Nanoparticles Synthesis and Applications
Authors: Amit Ghosh | Jiratheep Pruchyathamkorn | Carles Fuertes Espinosa | Jonathan R. Nitschke
Source: Journal of the American Chemical Society (article, 2024-01-17)
Field: Chemistry — Organic Chemistry

Light-Driven Purification of Progesterone from Steroid Mixtures Using a Photoresponsive Metal–Organic Capsule Chemical separations are expensive, consuming 10-15% of humanity's global energy budget. Many current separation methods employ thermal energy for distillation, often through the combustion of carbon-containing fuels, or extractions and crystallizations from organic solvents, which must then be discarded or redistilled, with a substantial energetic cost. The direct use of renewable energy sources, such as light, could enable the development of novel separations processes, as is required for the transition away from fossil fuel use. Metal-organic capsules, which can selectively bind molecules from mixtures, can provide the foundation for these novel separations processes. Here we report a tetrahedral metal-organic capsule bearing light-responsive diazo moieties around its metal-ion vertices. This capsule can be used to selectively separate progesterone from a mixture of steroids in a process driven by visible light energy. Our process combines biphasic extraction and selective binding of progesterone with the light-driven release of this molecule in purified form. Ultimately, our process might be adapted to the purifications of the many other fine chemical products that are bound selectively by capsules. Chemistry ; Molecule ; Metal ; Organic molecules ; Combinatorial chemistry ; Chemical engineering ; Organic chemistry Supramolecular Chemistry and Complexes ; Metal-Organic Frameworks: Synthesis and Applications ; Porphyrin and Phthalocyanine Chemistry
Authors: Sanghyun Jeon | Yash Kamble | Haisu Kang | Jiachun Shi | Matthew A. Wade | Bijal B. Patel | Tianyuan Pan | Simon A. Rogers | Charles E. Sing | Damien Guironnet | Ying Diao
Source: Proceedings of the National Academy of Sciences (article, 2024-02-20)
Field: Materials Science — Materials Chemistry

Direct-ink-write cross-linkable bottlebrush block copolymers for on-the-fly control of structural color Additive manufacturing capable of controlling and dynamically modulating structures down to the nanoscopic scale remains challenging. By marrying additive manufacturing with self-assembly, we develop a UV (ultra-violet)-assisted direct ink write approach for on-the-fly modulation of structural color by programming the assembly kinetics through photo-cross-linking. We design a photo-cross-linkable bottlebrush block copolymer solution as a printing ink that exhibits vibrant structural color (i.e., photonic properties) due to the nanoscopic lamellar structures formed post extrusion. By dynamically modulating UV-light irradiance during printing, we can program the color of the printed material to access a broad spectrum of visible light with a single ink while also creating color gradients not previously possible. We unveil the mechanism of this approach using a combination of coarse-grained simulations, rheological measurements, and structural characterizations. Central to the assembly mechanism is the matching of the cross-linking timescale with the assembly timescale, which leads to kinetic trapping of the assembly process that evolves structural color from blue to red driven by solvent evaporation. This strategy of integrating cross-linking chemistry and out-of-equilibrium processing opens an avenue for spatiotemporal control of self-assembled nanostructures during additive manufacturing. Structural coloration ; Inkwell ; Nanoscopic scale ; Materials science ; Nanostructure ; Copolymer ; Lamellar structure ; Nanotechnology ; Polymer ; Optoelectronics ; Composite material ; Photonic crystal Block Copolymer Self-Assembly ; Advanced Polymer Synthesis and Characterization ; Nanofabrication and Lithography Techniques
Authors: Luis Alejandro Martínez-Chávez | Mariana Y. Hernández-Ramírez | Ana Angélica Feregrino‐Pérez | Karen Esquivel
Source: Agronomy (article, 2024-11-27)
Field: Materials Science — Materials Chemistry

Cutting-Edge Strategies to Enhance Bioactive Compound Production in Plants: Potential Value of Integration of Elicitation, Metabolic Engineering, and Green Nanotechnology The increasing demand for bioactive compounds from plants has spurred the exploration of innovative strategies to enhance their production. This review focuses on three advanced methodologies: elicitation, metabolic engineering, and using biosynthesized nanoparticles. Elicitation, through the application of abiotic and biotic stressors, stimulates plant defense mechanisms, and significantly boosts secondary metabolite production. Metabolic engineering further refines this process by genetically modifying key pathways to redirect resources towards the synthesis of valuable compounds. Additionally, biosynthesized metallic and metallic oxide nanoparticles offer a sustainable method to enhance plant growth and metabolite production. These nanoparticles act not only as elicitors, but also catalyze metabolic processes and improve the efficiency of bioactive compound synthesis. Despite significant advancements, challenges remain in optimizing these approaches for large-scale agricultural application, particularly in understanding the precise interactions between nanoparticles and plant systems. The integration of these strategies holds great potential for sustainable agriculture and the development of high-value phytochemicals. Future research should focus on unraveling the complex mechanisms behind these interactions and refining these methods for commercial use, ensuring their environmental and economic sustainability. Production (economics) ; Nanotechnology ; Biochemical engineering ; Value (mathematics) ; Biotechnology ; Engineering ; Computer science ; Materials science ; Biology Nanoparticles: synthesis and applications ; Plant tissue culture and regeneration ; Magnetic and Electromagnetic Effects
Authors: Akio Yamauchi | Kentaro TANAKA | Masaaki Fuki | Saiya Fujiwara | Nobuo Kimizuka | Tomohiro Ryu | Masaki Saigo | Ken Onda | Ryota Kusumoto | Nami Ueno | Harumi Sato | Yasuhiro Kobori | Kiyoshi Miyata | Nobuhiro Yanai
Source: Science Advances (article, 2024-01-03)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Room-temperature quantum coherence of entangled multiexcitons in a metal-organic framework Singlet fission can generate an exchange-coupled quintet triplet pair state <sup>5</sup>TT, which could lead to the realization of quantum computing and quantum sensing using entangled multiple qubits even at room temperature. However, the observation of the quantum coherence of <sup>5</sup>TT has been limited to cryogenic temperatures, and the fundamental question is what kind of material design will enable its room-temperature quantum coherence. Here, we show that the quantum coherence of singlet fission-derived <sup>5</sup>TT in a chromophore-integrated metal-organic framework can be over hundred nanoseconds at room temperature. The suppressed motion of the chromophores in ordered domains within the metal-organic framework leads to the enough fluctuation of the exchange interaction necessary for <sup>5</sup>TT generation but, at the same time, does not cause severe <sup>5</sup>TT decoherence. Furthermore, the phase and amplitude of quantum beating depend on the molecular motion, opening the way to room-temperature molecular quantum computing based on multiple quantum gate control. Coherence (philosophical gambling strategy) ; Quantum ; Quantum decoherence ; Qubit ; Physics ; Chromophore ; Quantum mechanics ; Singlet state ; Quantum technology ; Chemical physics ; Chemistry ; Open quantum system Organic and Molecular Conductors Research ; Spectroscopy and Quantum Chemical Studies ; Magnetism in coordination complexes
Authors: David Ohayon | Glenn Quek | Benjamin Rui Peng Yip | Fernando López‐García | Pei Rou Ng | Ricardo Javier Vázquez | Daria V. Andreeva | Xuehang Wang | Guillermo C. Bazan
Source: Advanced Materials (article, 2024-09-30)
Field: Materials Science — Electronic, Optical and Magnetic Materials

High‐Performance Aqueous Supercapacitors Based on a Self‐Doped n‐Type Conducting Polymer Environmentally-benign materials play a pivotal role in advancing the scalability of energy storage devices. In particular, conjugated polymers constitute a potentially greener alternative to inorganic- and carbon-based materials. One challenge to wider implementation is the scarcity of n-doped conducting polymers to achieve full cells with high-rate performance. Herein, this work demonstrates the use of a self-doped n-doped conjugated polymer, namely poly(benzodifurandione) (PBDF), for fabricating aqueous supercapacitors. PBDF demonstrates a specific capacitance of 202 ± 3 F g<sup>-1</sup>, retaining 81% of the initial performance over 5000 cycles at 10 A g<sup>-1</sup> in 2 m NaCl<sub>(</sub> <sub>aq</sub> <sub>)</sub>. PBDF demonstrates rate performances of up to 100 and 50 A g<sup>-1</sup> at 1 and 2 mg cm<sup>-2</sup>, respectively. Electrochemical impedance analysis reveals a surface-mediated charge storage mechanism. Improvements can be achieved by adding reduced graphene oxide (rGO), thereby obtaining a specific capacitance of 288 ± 8 F g<sup>-1</sup> and high-rate operation (270 A g<sup>-1</sup>). The performance of PBDF is examined in symmetric and asymmetric membrane-less cells, demonstrating high-rate performance, while retaining 83% of the initial capacitance after 100 000 cycles at 10 A g<sup>-1</sup>. PBDF thus offers new prospects for energy storage applications, showcasing both desirable performance and stability without the need for additives or binders and relying on environmentally friendly solutions. Supercapacitor ; Materials science ; Capacitance ; Graphene ; Polymer ; Environmentally friendly ; Energy storage ; Chemical engineering ; Aqueous solution ; Doping ; Nanotechnology ; Oxide ; Electrochemistry ; Conductive polymer ; Optoelectronics ; Composite material ; Organic chemistry ; Electrode ; Chemistry Supercapacitor Materials and Fabrication ; Conducting polymers and applications ; Advanced battery technologies research
Authors: Shaolou Wei | Kyung‐Shik Kim | John Foltz | Cemal Cem Taşan
Source: Advanced Materials (article, 2024-06-06)
Field: Materials Science — Materials Chemistry

Discovering Pyramidal Treasures: Multi‐Scale Design of High Strength–Ductility Titanium Alloys Mechanical properties of titanium alloys, one of humankind's most essential structural materials, suffer from the lack of 〈c + a〉 dislocations on pyramidal slip planes, failing homogeneous plastic strain accommodation. This mechanical treasure is not easily accessible in titanium alloys because of the required excessively high stress levels. The present work demonstrates that such a dilemma may be overcome by meticulously tuning the c/a ratio, the simplest crystallographic parameter of the hexagonal close-packed lattice, through Sn alloying. Combining this lattice-scale design concept with a cross-rolling based polycrystal-scale design solution, this study showcases a facile route to bimodal (α + β) titanium alloys with exceptional strength-ductility synergy. Materials science ; Titanium ; Ductility (Earth science) ; Titanium alloy ; Slip (aerodynamics) ; Shape-memory alloy ; Lattice (music) ; Hexagonal crystal system ; Homogeneous ; Composite material ; Metallurgy ; Alloy ; Crystallography ; Thermodynamics Titanium Alloys Microstructure and Properties ; Corrosion Behavior and Inhibition ; Metal and Thin Film Mechanics
Authors: Peichen Zhong | Bowen Deng | Tanjin He | Zhengyan Lun | Gerbrand Ceder
Source: Joule (article, 2024-04-10)
Field: Materials Science — Materials Chemistry

Deep learning of experimental electrochemistry for battery cathodes across diverse compositions Cathode ; Battery (electricity) ; Computer science ; Fidelity ; Representation (politics) ; Electrochemistry ; High fidelity ; Materials science ; Artificial intelligence ; Nanotechnology ; Electrical engineering ; Engineering ; Chemistry ; Physics ; Electrode ; Power (physics) ; Politics Machine Learning in Materials Science ; Advancements in Battery Materials ; Advanced Battery Technologies Research
Authors: Joachim Sauer
Source: Journal of Catalysis (article, 2024-04-08)
Field: Materials Science — Materials Chemistry

The future of computational catalysis The future of computational heterogeneous catalysis is shaped by machine learning in two different but equally important areas: (i) development of atomistic potentials that closely approximate DFT and wavefunction based ab initio methods (MP2, CCSD(T)), but are computationally more efficient, and (ii) finding structure and reactivity descriptors for predicting catalytic materials and reactions. Machine Learning Potentials will enable improved sampling of the potential energy surface (PES) for reaction conditions, but they will not do better than the data on which they are trained. Therefore, this perspective focusses on ways of improving the quality of the PES beyond the generalized gradient approximation (GGA) climbing the Jacob’s ladder of functionals in DFT up to RPA and using wavefunction methods such as MP2 and CCSD(T). It is problem solving in close collaboration with experiment that has made computational methodology relevant and this remains an important aspect of data science methods in heterogeneous catalysis. Chemistry ; Wave function ; Ab initio ; Computational chemistry ; Potential energy surface ; Catalysis ; Perspective (graphical) ; Potential energy ; Statistical physics ; Quantum mechanics ; Computer science ; Artificial intelligence ; Physics ; Organic chemistry Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Topic Modeling
Authors: Pravat Ghorai | Udayan Mondal | Abhijit Hazra | Priyabrata Banerjee
Source: Coordination Chemistry Reviews (article, 2024-07-26)
Field: Chemistry — Inorganic Chemistry

Luminescent metal organic frameworks (LMOFs) and allied composites for the unveiling of organic environmental contaminants (explosive NACs, PAHs and EDCs) sensing through ‘Molecular Recognition’: A chronicle of recent penetration and future modelling Explosive material ; Chemistry ; Environmental chemistry ; Contamination ; Penetration (warfare) ; Metal-organic framework ; Organic chemistry ; Ecology Metal-Organic Frameworks: Synthesis and Applications ; Molecular Sensors and Ion Detection ; Luminescence and Fluorescent Materials
Authors: Qiaomu Yang | Aikaterini Vriza | Cesar A. Castro Rubio | Henry Chan | Yukun Wu | Jie Xu
Source: Chemistry of Materials (article, 2024-01-29)
Field: Materials Science — Polymers and Plastics

Artificial Intelligence for Conjugated Polymers Conjugated polymers have garnered significant attention due to their diverse applications in electronics, photonics, and energy storage. However, realizing their full potential poses a formidable challenge, as their design has historically relied on iterative adjustments and continuous inspiration from researchers. Traditional methods often struggle to efficiently navigate their vast chemical landscape. Herein, the application of artificial intelligence (AI), specifically machine learning (ML), needs to be discussed in the realm of conjugated polymers. Our paper emphasizes the importance of understanding the structure–property relationships of these polymers and how ML can facilitate property prediction and inverse-design. We delve into various chemical fingerprints, structural descriptors, and ML algorithms, showcasing their utility across a spectrum of applications, including simulations, glass transition temperature determination, photovoltaics, reorganization energy for charge transport, photocatalysts, and sensors. Finally, we give some outlooks in this filed and propose unexplored areas within the field that hold the potential to benefit from ML techniques. Conjugated system ; Computer science ; Polymer ; Nanotechnology ; Photovoltaics ; Realm ; Property (philosophy) ; Artificial intelligence ; Biochemical engineering ; Materials science ; Photovoltaic system ; Engineering ; Electrical engineering Conducting polymers and applications ; Machine Learning in Materials Science ; Fuel Cells and Related Materials
Authors: Qadeer Akbar Sial | Usman Safder | Shahid Iqbal | Rana Basit Ali
Source: Sustainability (article, 2024-02-10)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Advancement in Supercapacitors for IoT Applications by Using Machine Learning: Current Trends and Future Technology Supercapacitors (SCs) are gaining attention for Internet of Things (IoT) devices because of their impressive characteristics, including their high power and energy density, extended lifespan, significant cycling stability, and quick charge–discharge cycles. Hence, it is essential to make precise predictions about the capacitance and lifespan of supercapacitors to choose the appropriate materials and develop plans for replacement. Carbon-based supercapacitor electrodes are crucial for the advancement of contemporary technology, serving as a key component among numerous types of electrode materials. Moreover, accurately forecasting the lifespan of energy storage devices may greatly improve the efficient handling of system malfunctions. Researchers worldwide have increasingly shown interest in using machine learning (ML) approaches for predicting the performance of energy storage materials. The interest in machine learning is driven by its noteworthy benefits, such as improved accuracy in predictions, time efficiency, and cost-effectiveness. This paper reviews different charge storage processes, categorizes SCs, and investigates frequently employed carbon electrode components. The performance of supercapacitors, which is crucial for Internet of Things (IoT) applications, is affected by a number of their characteristics, including their power density, charge storage capacity, and cycle longevity. Additionally, we provide an in-depth review of several recently developed ML-driven models used for predicting energy substance properties and optimizing supercapacitor effectiveness. The purpose of these proposed ML algorithms is to validate their anticipated accuracies, aid in the selection of models, and highlight future research topics in the field of scientific computing. Overall, this research highlights the possibility of using ML techniques to make significant advancements in the field of energy-storing device development. Supercapacitor ; Energy storage ; Computer science ; Capacitance ; Component (thermodynamics) ; Internet of Things ; Power (physics) ; Reliability engineering ; Engineering ; Embedded system ; Electrode Supercapacitor Materials and Fabrication ; Conducting polymers and applications ; Advanced Battery Technologies Research
Authors: Sebastien Röcken | Julija Zavadlav
Source: npj Computational Materials (article, 2024-04-05)
Field: Materials Science — Materials Chemistry

Accurate machine learning force fields via experimental and simulation data fusion Abstract Machine Learning (ML)-based force fields are attracting ever-increasing interest due to their capacity to span spatiotemporal scales of classical interatomic potentials at quantum-level accuracy. They can be trained based on high-fidelity simulations or experiments, the former being the common case. However, both approaches are impaired by scarce and erroneous data resulting in models that either do not agree with well-known experimental observations or are under-constrained and only reproduce some properties. Here we leverage both Density Functional Theory (DFT) calculations and experimentally measured mechanical properties and lattice parameters to train an ML potential of titanium. We demonstrate that the fused data learning strategy can concurrently satisfy all target objectives, thus resulting in a molecular model of higher accuracy compared to the models trained with a single data source. The inaccuracies of DFT functionals at target experimental properties were corrected, while the investigated off-target properties were affected only mildly and mostly positively. Our approach is applicable to any material and can serve as a general strategy to obtain highly accurate ML potentials. Fusion ; Computer science ; Artificial intelligence ; Machine learning Machine Learning in Materials Science ; Advanced Materials Characterization Techniques ; Computational Drug Discovery Methods