jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Francis J. O’Reilly | Andrea Graziadei | Christian Forbrig | Rica Bremenkamp | Kristine Charles | Swantje Lenz | Christoph Elfmann | Lutz Fischer | Jörg Stülke | Juri Rappsilber
Source: Molecular Systems Biology (article, 2023-02-23)
Field: Chemistry — Spectroscopy

Protein complexes in cells by <scp>AI</scp> ‐assisted structural proteomics Accurately modeling the structures of proteins and their complexes using artificial intelligence is revolutionizing molecular biology. Experimental data enable a candidate-based approach to systematically model novel protein assemblies. Here, we use a combination of in-cell crosslinking mass spectrometry and co-fractionation mass spectrometry (CoFrac-MS) to identify protein-protein interactions in the model Gram-positive bacterium Bacillus subtilis. We show that crosslinking interactions prior to cell lysis reveals protein interactions that are often lost upon cell lysis. We predict the structures of these protein interactions and others in the SubtiWiki database with AlphaFold-Multimer and, after controlling for the false-positive rate of the predictions, we propose novel structural models of 153 dimeric and 14 trimeric protein assemblies. Crosslinking MS data independently validates the AlphaFold predictions and scoring. We report and validate novel interactors of central cellular machineries that include the ribosome, RNA polymerase, and pyruvate dehydrogenase, assigning function to several uncharacterized proteins. Our approach uncovers protein-protein interactions inside intact cells, provides structural insight into their interaction interfaces, and is applicable to genetically intractable organisms, including pathogenic bacteria. Biology ; Proteomics ; Computational biology ; Cell biology ; Biochemistry ; Gene Advanced Proteomics Techniques and Applications ; Cell Image Analysis Techniques ; Genetics, Bioinformatics, and Biomedical Research
Authors: Atefeh Zarepour | Sepideh Ahmadi | Navid Rabiee | Ali Zarrabi | Siavash Iravani
Source: Nano-Micro Letters (review, 2023-04-13)
Field: Materials Science — Materials Chemistry

Self-Healing MXene- and Graphene-Based Composites: Properties and Applications Today, self-healing graphene- and MXene-based composites have attracted researchers due to the increase in durability as well as the cost reduction in long-time applications. Different studies have focused on designing novel self-healing graphene- and MXene-based composites with enhanced sensitivity, stretchability, and flexibility as well as improved electrical conductivity, healing efficacy, mechanical properties, and energy conversion efficacy. These composites with self-healing properties can be employed in the field of wearable sensors, supercapacitors, anticorrosive coatings, electromagnetic interference shielding, electronic-skin, soft robotics, etc. However, it appears that more explorations are still needed to achieve composites with excellent arbitrary shape adaptability, suitable adhesiveness, ideal durability, high stretchability, immediate self-healing responsibility, and outstanding electromagnetic features. Besides, optimizing reaction/synthesis conditions and finding suitable strategies for functionalization/modification are crucial aspects that should be comprehensively investigated. MXenes and graphene exhibited superior electrochemical properties with abundant surface terminations and great surface area, which are important to evolve biomedical and sensing applications. However, flexibility and stretchability are important criteria that need to be improved for their future applications. Herein, the most recent advancements pertaining to the applications and properties of self-healing graphene- and MXene-based composites are deliberated, focusing on crucial challenges and future perspectives. Graphene ; Materials science ; Self-healing ; Durability ; Flexibility (engineering) ; Supercapacitor ; Surface modification ; Composite material ; MXenes ; Nanotechnology ; Mechanical engineering ; Electrochemistry ; Electrode MXene and MAX Phase Materials ; Advanced Sensor and Energy Harvesting Materials ; Supercapacitor Materials and Fabrication
Authors: Yuexing Cheng | Junhao Ye | Li Lai | Shi Fang | Daoyou Guo
Source: Advanced Electronic Materials (article, 2023-02-07)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Ambipolarity Regulation of Deep‐UV Photocurrent by Controlling Crystalline Phases in Ga<sub>2</sub>O<sub>3</sub> Nanostructure for Switchable Logic Applications Abstract Photoelectrochemical photocurrent switching (PEPS) effect can regulate the polarity of photocurrent, which has significant potential applications in areas such as logic gates, photosynapse, and artificial intelligence. In this work, it is reported for the first time that a pure Ga 2 O 3 photoelectrochemical system exhibits ambipolar photocurrent behavior induced by deep ultraviolet, which is closely linked to the crystalline phase of Ga 2 O 3 (α or β) and the surface states of oxygen vacancies. Spongy porous nanorod arrays (NRAs) of Ga 2 O 3 designed here not only increase the contact area of Ga 2 O 3 with the electrolyte but also can lower largely the reflection of light and improve light‐trapping capacity. For α phase Ga 2 O 3 , the photocurrent is in a forward direction under positive bias and shows a backward direction under negative bias in NaOH solution, exhibiting a distinct ambipolar photocurrent phenomenon, which can be attributed to more oxygen vacancy surface states and lower potential barrier at the semiconductor/electrolyte interface. Furtherly, the effect of the surface states on the ambipolar photocurrent behavior of α‐Ga 2 O 3 NRAs is demonstrated by various treatment times of oxygen plasma, whose switching point moves from 0 V to −0.19 V with treatment for 30 min and continues to move in the negative direction with the increase of treatment time. Moreover, based on the ambipolar photocurrent behavior of α‐Ga 2 O 3 NRAs, adjustable Boolean logic gates with voltage are prepared as the input source, offering a new path for the photoelectric device multifunctional integration needed in the Post‐Moore era, with a high accuracy manipulated by solar‐blind deep ultraviolet light. Photocurrent ; Ambipolar diffusion ; Materials science ; Optoelectronics ; Nanotechnology ; Physics ; Electron Ga2O3 and related materials ; Advanced Photocatalysis Techniques ; ZnO doping and properties
Authors: Wei Jiang | Qing Li | Ruofei Zhang | Jianru Li | Qianyu Lin | Jingyun Li | Xinyao Zhou | Xiyun Yan | Kelong Fan
Source: Nature Communications (article, 2023-12-08)
Field: Materials Science — Materials Chemistry

Chiral metal-organic frameworks incorporating nanozymes as neuroinflammation inhibitors for managing Parkinson’s disease Nanomedicine-based anti-neuroinflammation strategy has become a promising dawn of Parkinson's disease (PD) treatment. However, there are significant gaps in our understanding of the therapeutic mechanisms of antioxidant nanomedicines concerning the pathways traversing the blood-brain barrier (BBB) and subsequent inflammation mitigation. Here, we report nanozyme-integrated metal-organic frameworks with excellent antioxidant activity and chiral-dependent BBB transendocytosis as anti-neuroinflammatory agents for the treatment of PD. These chiral nanozymes are synthesized by embedding ultra-small platinum nanozymes (Ptzymes) into L-chiral and D-chiral imidazolate zeolite frameworks (Ptzyme@L-ZIF and Ptzyme@D-ZIF). Compared to Ptzyme@L-ZIF, Ptzyme@D-ZIF shows higher accumulation in the brains of male PD mouse models due to longer plasma residence time and more pathways to traverse BBB, including clathrin-mediated and caveolae-mediated endocytosis. These factors contribute to the superior therapeutic efficacy of Ptzyme@D-ZIF in reducing behavioral disorders and pathological changes. Bioinformatics and biochemical analyses suggest that Ptzyme@D-ZIF inhibits neuroinflammation-induced apoptosis and ferroptosis in damaged neurons. The research uncovers the biodistribution, metabolic variances, and therapeutic outcomes of nanozymes-integrated chiral ZIF platforms, providing possibilities for devising anti-PD drugs. Neuroinflammation ; Nanomedicine ; Zeolitic imidazolate framework ; Metal-organic framework ; Chemistry ; Pharmacology ; Nanotechnology ; Inflammation ; Neuroscience ; Medicine ; Materials science ; Biology ; Nanoparticle ; Immunology Advanced Nanomaterials in Catalysis ; Nanocluster Synthesis and Applications ; Nanoplatforms for cancer theranostics
Authors: Zhiling Zheng | Oufan Zhang | Ha L. Nguyen | Nakul Rampal | Ali H. Alawadhi | Zichao Rong | Teresa Head‐Gordon | Christian Borgs | Jennifer Chayes | Omar M. Yaghi
Source: ACS Central Science (article, 2023-11-10)
Field: Materials Science — Materials Chemistry

ChatGPT Research Group for Optimizing the Crystallinity of MOFs and COFs We leveraged the power of ChatGPT and Bayesian optimization in the development of a multi-AI-driven system, backed by seven large language model-based assistants and equipped with machine learning algorithms, that seamlessly orchestrates a multitude of research aspects in a chemistry laboratory (termed the ChatGPT Research Group). Our approach accelerated the discovery of optimal microwave synthesis conditions, enhancing the crystallinity of MOF-321, MOF-322, and COF-323 and achieving the desired porosity and water capacity. In this system, human researchers gained assistance from these diverse AI collaborators, each with a unique role within the laboratory environment, spanning strategy planning, literature search, coding, robotic operation, labware design, safety inspection, and data analysis. Such a comprehensive approach enables a single researcher working in concert with AI to achieve productivity levels analogous to those of an entire traditional scientific team. Furthermore, by reducing human biases in screening experimental conditions and deftly balancing the exploration and exploitation of synthesis parameters, our Bayesian search approach precisely zeroed in on optimal synthesis conditions from a pool of 6 million within a significantly shortened time scale. This work serves as a compelling proof of concept for an AI-driven revolution in the chemistry laboratory, painting a future where AI becomes an efficient collaborator, liberating us from routine tasks to focus on pushing the boundaries of innovation. Computer science ; Artificial intelligence ; Data science ; Machine learning Machine Learning in Materials Science ; Metal-Organic Frameworks: Synthesis and Applications ; Advanced Photocatalysis Techniques
Authors: Jie Zhuang | Adam C. Midgley | Yonghua Wei | Qiqi Liu | Deling Kong | Xinglu Huang
Source: Advanced Materials (review, 2023-01-26)
Field: Materials Science — Materials Chemistry

Machine‐Learning‐Assisted Nanozyme Design: Lessons from Materials and Engineered Enzymes Abstract Nanozymes are nanomaterials that exhibit enzyme‐like biomimicry. In combination with intrinsic characteristics of nanomaterials, nanozymes have broad applicability in materials science, chemical engineering, bioengineering, biochemistry, and disease theranostics. Recently, the heterogeneity of published results has highlighted the complexity and diversity of nanozymes in terms of consistency of catalytic capacity. Machine learning (ML) shows promising potential for discovering new materials, yet it remains challenging for the design of new nanozymes based on ML approaches. Alternatively, ML is employed to promote optimization of intelligent design and application of catalytic materials and engineered enzymes. Incorporation of the successful ML algorithms used in the intelligent design of catalytic materials and engineered enzymes can concomitantly facilitate the guided development of next‐generation nanozymes with desirable properties. Here, recent progress in ML, its utilization in the design of catalytic materials and enzymes, and how emergent ML applications serve as promising strategies to circumvent challenges associated with time‐expensive and laborious testing in nanozyme research and development are summarized. The potential applications of successful examples of ML‐aided catalytic materials and engineered enzymes in nanozyme design are also highlighted, with special focus on the unified aims in enhancing design and recapitulation of substrate selectivity and catalytic activity. Nanotechnology ; Nanomaterials ; Biochemical engineering ; Computer science ; Materials science ; Engineering Advanced Nanomaterials in Catalysis ; Nanocluster Synthesis and Applications ; Nanoparticles: synthesis and applications
Authors: Silvia Badini | Stefano Regondi | Raffaele Pugliese
Source: Materials (review, 2023-08-30)
Field: Materials Science — Materials Chemistry

Unleashing the Power of Artificial Intelligence in Materials Design The integration of artificial intelligence (AI) algorithms in materials design is revolutionizing the field of materials engineering thanks to their power to predict material properties, design de novo materials with enhanced features, and discover new mechanisms beyond intuition. In addition, they can be used to infer complex design principles and identify high-quality candidates more rapidly than trial-and-error experimentation. From this perspective, herein we describe how these tools can enable the acceleration and enrichment of each stage of the discovery cycle of novel materials with optimized properties. We begin by outlining the state-of-the-art AI models in materials design, including machine learning (ML), deep learning, and materials informatics tools. These methodologies enable the extraction of meaningful information from vast amounts of data, enabling researchers to uncover complex correlations and patterns within material properties, structures, and compositions. Next, a comprehensive overview of AI-driven materials design is provided and its potential future prospects are highlighted. By leveraging such AI algorithms, researchers can efficiently search and analyze databases containing a wide range of material properties, enabling the identification of promising candidates for specific applications. This capability has profound implications across various industries, from drug development to energy storage, where materials performance is crucial. Ultimately, AI-based approaches are poised to revolutionize our understanding and design of materials, ushering in a new era of accelerated innovation and advancement. Materials informatics ; Computer science ; Artificial intelligence ; Intuition ; Data science ; Field (mathematics) ; Systems engineering ; Engineering informatics ; Engineering Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Electronic and Structural Properties of Oxides
Authors: Shaojun You | Mimi Zhou | Lei Xu | Deliang Chen | Menghui Fan | Jing Huang | Wenbin Ma | Shengyun Luo | Mohsen Rahmani | Chaobiao Zhou | Andrey E. Miroshnichenko | Lujun Huang
Source: Nanophotonics (article, 2023-04-28)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Quasi‐bound states in the continuum with a stable resonance wavelength in dimer dielectric metasurfaces Symmetry-protected bound states in the continuum (SP-BICs) are one of the most intensively studied BICs. Typically, SP-BICs must be converted into quasi-BICs (QBICs) by breaking the unit cell's symmetry so that they can be accessed by the external excitation. The symmetry-broken usually results in a varied resonance wavelength of QBICs which are also highly sensitive to the asymmetry parameters. In this work, we demonstrate that QBICs with a stable resonance wavelength can be realized by breaking translational symmetry in an all-dielectric metasurface. The unit cell of metasurface is made of a silicon nanodisk dimer. The Q-factor of QBICs is precisely tuned by changing the interspacing of two nanodisks while their resonance wavelength is quite stable against the interspacing. We also find that such BICs show weak dependence on the shape of the nanodisk. Multiple decompositions indicate that the toroidal dipole dominates this type of QBIC. The resonance wavelengths of QBICs can be tuned only by changing either the lattice constants or the radius of nanodisk. Finally, we present experimental demonstrations on such a QBIC with a stable resonance wavelength. The highest measured Q-factor of QBICs is >3000. Our results may find promising applications in enhancing light-matter interaction. Dielectric ; Dimer ; Bound state ; Wavelength ; Nanomaterials ; Resonance (particle physics) ; Physics ; Materials science ; Condensed matter physics ; Optoelectronics ; Quantum mechanics ; Nanotechnology ; Nuclear magnetic resonance Metamaterials and Metasurfaces Applications ; Plasmonic and Surface Plasmon Research ; Advanced Antenna and Metasurface Technologies
Authors: Bo Peng | Ye Wei | Yu Qin | Jiabao Dai | Yue Li | Aobo Liu | Yun Tian | Liuliu Han | Yufeng Zheng | Peng Wen
Source: Nature Communications (article, 2023-10-19)
Field: Materials Science — Materials Chemistry

Machine learning-enabled constrained multi-objective design of architected materials Architected materials that consist of multiple subelements arranged in particular orders can demonstrate a much broader range of properties than their constituent materials. However, the rational design of these materials generally relies on experts' prior knowledge and requires painstaking effort. Here, we present a data-efficient method for the high-dimensional multi-property optimization of 3D-printed architected materials utilizing a machine learning (ML) cycle consisting of the finite element method (FEM) and 3D neural networks. Specifically, we apply our method to orthopedic implant design. Compared to uniform designs, our experience-free method designs microscale heterogeneous architectures with a biocompatible elastic modulus and higher strength. Furthermore, inspired by the knowledge learned from the neural networks, we develop machine-human synergy, adapting the ML-designed architecture to fix a macroscale, irregularly shaped animal bone defect. Such adaptation exhibits 20% higher experimental load-bearing capacity than the uniform design. Thus, our method provides a data-efficient paradigm for the fast and intelligent design of architected materials with tailored mechanical, physical, and chemical properties. Microscale chemistry ; Computer science ; Finite element method ; Load bearing ; Artificial neural network ; Property (philosophy) ; Adaptation (eye) ; Artificial intelligence ; Materials science ; Structural engineering ; Engineering ; Composite material Machine Learning in Materials Science ; Additive Manufacturing and 3D Printing Technologies ; Cellular and Composite Structures
Authors: Lorena Vázquez‐Iglesias | Giovanna Maria Stanfoca Casagrande | Daniel García‐Lojo | Letícia Ferro Leal | Tien Anh Ngo | Jorge Pérez‐Juste | Rui Manuel Reis | Krishna Kant | Isabel Pastoriza‐Santos
Source: Bioactive Materials (review, 2023-12-31)
Field: Materials Science — Electronic, Optical and Magnetic Materials

SERS sensing for cancer biomarker: Approaches and directions These days, cancer is thought to be more than just one illness, with several complex subtypes that require different screening approaches. These subtypes can be distinguished by the distinct markings left by metabolites, proteins, miRNA, and DNA. Personalized illness management may be possible if cancer is categorized according to its biomarkers. In order to stop cancer from spreading and posing a significant risk to patient survival, early detection and prompt treatment are essential. Traditional cancer screening techniques are tedious, time-consuming, and require expert personnel for analysis. This has led scientists to reevaluate screening methodologies and make use of emerging technologies to achieve better results. Using time and money saving techniques, these methodologies integrate the procedures from sample preparation to detection in small devices with high accuracy and sensitivity. With its proven potential for biomedical use, surface-enhanced Raman scattering (SERS) has been widely used in biosensing applications, particularly in biomarker identification. Consideration was given especially to the potential of SERS as a portable clinical diagnostic tool. The approaches to SERS-based sensing technologies for both invasive and non-invasive samples are reviewed in this article, along with sample preparation techniques and obstacles. Aside from these significant constraints in the detection approach and techniques, the review also takes into account the complexity of biological fluids, the availability of biomarkers, and their sensitivity and selectivity, which are generally lowered. Massive ways to maintain sensing capabilities in clinical samples are being developed recently to get over this restriction. SERS is known to be a reliable diagnostic method for treatment judgments. Nonetheless, there is still room for advancement in terms of portability, creation of diagnostic apps, and interdisciplinary AI-based applications. Therefore, we will outline the current state of technological maturity for SERS-based cancer biomarker detection in this article. The review will meet the demand for reviewing various sample types (invasive and non-invasive) of cancer biomarkers and their detection using SERS. It will also shed light on the growing body of research on portable methods for clinical application and quick cancer detection. Cancer detection ; Computer science ; Biomarker ; Nanotechnology ; Cancer biomarkers ; Risk analysis (engineering) ; Cancer ; Identification (biology) ; Biochemical engineering ; Medicine ; Materials science ; Biology ; Engineering ; Internal medicine Gold and Silver Nanoparticles Synthesis and Applications ; Biosensors and Analytical Detection ; Advanced biosensing and bioanalysis techniques
Authors: Minhua Ai | Lun Pan | Chengxiang Shi | Zhen‐Feng Huang | Xiangwen Zhang | Wenbo Mi | Ji‐Jun Zou
Source: Nature Communications (article, 2023-07-28)
Field: Materials Science — Materials Chemistry

Spin selection in atomic-level chiral metal oxide for photocatalysis The spin degree of freedom is an important and intrinsic parameter in boosting carrier dynamics and surface reaction kinetics of photocatalysis. Here we show that chiral structure in ZnO can induce spin selectivity effect to promote photocatalytic performance. The ZnO crystals synthesized using chiral methionine molecules as symmetry-breaking agents show hierarchical chirality. Magnetic circular dichroism spectroscopic and magnetic conductive-probe atomic force microscopic measurements demonstrate that chiral structure acts as spin filters and induces spin polarization in photoinduced carriers. The polarized carriers not only possess the prolonged carrier lifetime, but also increase the triplet species instead of singlet byproducts during reaction. Accordingly, the left- and right-hand chiral ZnO exhibit 2.0- and 1.9-times higher activity in photocatalytic O<sub>2</sub> production and 2.5- and 2.0-times higher activities in contaminant photodegradation, respectively, compared with achiral ZnO. This work provides a feasible strategy to manipulate the spin properties in metal oxides for electron spin-related redox catalysis. Photocatalysis ; Selection (genetic algorithm) ; Oxide ; Metal ; Spin (aerodynamics) ; Materials science ; Chemical physics ; Nanotechnology ; Condensed matter physics ; Chemistry ; Physics ; Catalysis ; Computer science ; Metallurgy ; Thermodynamics Copper-based nanomaterials and applications ; ZnO doping and properties ; Transition Metal Oxide Nanomaterials