jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Xianglu Zhu | Rahel Suchintita Das | M.L. Bhavya | Marco García‐Vaquero | Brijesh K. Tiwari
Source: Ultrasonics Sonochemistry (review, 2024-03-16)
Field: Materials Science — Materials Chemistry

Acoustic cavitation for agri-food applications: Mechanism of action, design of new systems, challenges and strategies for scale-up Acoustic cavitation, an intriguing phenomenon resulting from the interaction of sound waves with a liquid medium, has emerged as a promising avenue in agri-food processing, offering opportunities to enhance established processes improving primary production of ingredients and further food processing. This comprehensive review provides an in-depth analysis of the mechanisms, design considerations, challenges and scale-up strategies associated with acoustic cavitation for agri-food applications. The paper starts by elucidating the fundamental principles of acoustic cavitation and its measurement, delving then into the diverse effects of different parameters associated with, the acoustic wave, mechanical design and operation of the ultrasonic system, along with those related to the food matrix. The technological advancements achieved in the design and set-up of ultrasonic reactors addressing limitations during scale up are also discussed. The design, engineering and mathematical modelling of ultrasonic equipment tailored for agri-food applications are explored, along with strategies to maximize cavitation intensity and efficiency in the application of brining, freezing, drying, emulsification, filtration and extraction. Advanced US equipment, such as multi-transducers (tubular resonator, FLOW:WAVE®) and larger processing surface areas through innovative designing (Barbell horn, Cascatrodes<sup>TM</sup>), are one of the most promising strategies to ensure consistency of US operations at industrial scale. This review paper aims to provide valuable insights into harnessing acoustic cavitation's potential for up-scaling applications in food processing via critical examination of current research and advancements, while identifying future directions and opportunities for further research and innovation. Cavitation ; Ultrasonic sensor ; Acoustic streaming ; Food processing ; Computer science ; Scale (ratio) ; Process engineering ; Mechanical engineering ; Biochemical engineering ; Acoustics ; Nanotechnology ; Materials science ; Engineering ; Chemistry ; Physics Ultrasound and Cavitation Phenomena ; Microbial Inactivation Methods ; Magnetic and Electromagnetic Effects
Authors: Si Li | Xinxin Xu | Liguang Xu | Hengwei Lin | Hua Kuang | Chuanlai Xu
Source: Nature Communications (article, 2024-04-25)
Field: Materials Science — Materials Chemistry

Emerging trends in chiral inorganic nanomaterials for enantioselective catalysis Asymmetric transformations and synthesis have garnered considerable interest in recent decades due to the extensive need for chiral organic compounds in biomedical, agrochemical, chemical, and food industries. The field of chiral inorganic catalysts, garnering considerable interest for its contributions to asymmetric organic transformations, has witnessed remarkable advancements and emerged as a highly innovative research area. Here, we review the latest developments in this dynamic and emerging field to comprehensively understand the advances in chiral inorganic nanocatalysts and stimulate further progress in asymmetric catalysis. Enantioselective synthesis ; Nanomaterials ; Catalysis ; Nanotechnology ; Chemistry ; Materials science ; Organic chemistry Advanced Nanomaterials in Catalysis ; Advanced biosensing and bioanalysis techniques ; Nanocluster Synthesis and Applications
Authors: Jagdeep K. Virdi | Ashish Dusunge | Sachin Handa
Source: JACS Au (article, 2024-01-16)
Field: Materials Science — Materials Chemistry

Aqueous Micelles as Solvent, Ligand, and Reaction Promoter in Catalysis Water is considered to be the most sustainable and safest solvent. Micellar catalysis is a significant contributor to the chemistry in water. It promotes pathways involving water-sensitive intermediates and transient catalytic species under micelles' shielding effect while also replacing costly ligands and dipolar-aprotic solvents. However, there is a lack of critical information about micellar catalysis. This includes why it works better than traditional catalysis in organic solvents, why specific rules in micellar catalysis differ from those of conventional catalysis, and how the limitations of micellar catalysis can be addressed in the future. This Perspective aims to highlight the current gaps in our understanding of micellar catalysis and provide an analysis of designer surfactants' origin and essential components. This will also provide a fundamental understanding of micellar catalysis, including how aqueous micelles can simultaneously perform multiple functions such as solvent, ligand, and reaction promoter. Micelle ; Catalysis ; Solvent ; Ligand (biochemistry) ; Aqueous solution ; Chemistry ; Combinatorial chemistry ; Photochemistry ; Organic chemistry ; Biochemistry Mesoporous Materials and Catalysis ; Oxidative Organic Chemistry Reactions ; Nanomaterials for catalytic reactions
Authors: Alessandro Chiesa | P. Santini | Elena Garlatti | Fernando Luis | Stefano Carretta
Source: Reports on Progress in Physics (article, 2024-02-05)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Molecular nanomagnets: a viable path toward quantum information processing? Molecular nanomagnets (MNMs), molecules containing interacting spins, have been a playground for quantum mechanics. They are characterized by many accessible low-energy levels that can be exploited to store and process quantum information. This naturally opens the possibility of using them as qudits, thus enlarging the tools of quantum logic with respect to qubit-based architectures. These additional degrees of freedom recently prompted the proposal for encoding qubits with embedded quantum error correction (QEC) in single molecules. QEC is the holy grail of quantum computing and this qudit approach could circumvent the large overhead of physical qubits typical of standard multi-qubit codes. Another important strength of the molecular approach is the extremely high degree of control achieved in preparing complex supramolecular structures where individual qudits are linked preserving their individual properties and coherence. This is particularly relevant for building quantum simulators, controllable systems able to mimic the dynamics of other quantum objects. The use of MNMs for quantum information processing is a rapidly evolving field which still requires to be fully experimentally explored. The key issues to be settled are related to scaling up the number of qudits/qubits and their individual addressing. Several promising possibilities are being intensively explored, ranging from the use of single-molecule transistors or superconducting devices to optical readout techniques. Moreover, new tools from chemistry could be also at hand, like the chiral-induced spin selectivity. In this paper, we will review the present status of this interdisciplinary research field, discuss the open challenges and envisioned solution paths which could finally unleash the very large potential of molecular spins for quantum technologies. Qubit ; Physics ; Quantum computer ; Quantum information ; Quantum ; Coherence (philosophical gambling strategy) ; Quantum mechanics ; Quantum technology ; Flux qubit ; Quantum error correction ; Topology (electrical circuits) ; Nanotechnology ; Open quantum system ; Electrical engineering Magnetism in coordination complexes ; Advanced NMR Techniques and Applications ; Quantum and electron transport phenomena
Authors: Hyun Park | Xiaoli Yan | Ruijie Zhu | E. A. Huerta | Santanu Chaudhuri | Donny Cooper | Ian Foster | Emad Tajkhorshid
Source: Communications Chemistry (article, 2024-02-14)
Field: Chemistry — Inorganic Chemistry

A generative artificial intelligence framework based on a molecular diffusion model for the design of metal-organic frameworks for carbon capture Metal-organic frameworks (MOFs) exhibit great promise for CO<sub>2</sub> capture. However, finding the best performing materials poses computational and experimental grand challenges in view of the vast chemical space of potential building blocks. Here, we introduce GHP-MOFassemble, a generative artificial intelligence (AI), high performance framework for the rational and accelerated design of MOFs with high CO<sub>2</sub> adsorption capacity and synthesizable linkers. GHP-MOFassemble generates novel linkers, assembled with one of three pre-selected metal nodes (Cu paddlewheel, Zn paddlewheel, Zn tetramer) into MOFs in a primitive cubic topology. GHP-MOFassemble screens and validates AI-generated MOFs for uniqueness, synthesizability, structural validity, uses molecular dynamics simulations to study their stability and chemical consistency, and crystal graph neural networks and Grand Canonical Monte Carlo simulations to quantify their CO<sub>2</sub> adsorption capacities. We present the top six AI-generated MOFs with CO<sub>2</sub> capacities greater than 2m mol g<sup>-1</sup>, i.e., higher than 96.9% of structures in the hypothetical MOF dataset. Metal-organic framework ; Computer science ; Adsorption ; Materials science ; Nanotechnology ; Topology (electrical circuits) ; Chemistry ; Mathematics ; Organic chemistry Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; Carbon dioxide utilization in catalysis
Authors: Vibhas Chugh | Adreeja Basu | Ajeet Kaushik | Manshu | Shekhar Bhansali | Aviru Kumar Basu
Source: Nanoscale (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

Employing nano-enabled artificial intelligence (AI)-based smart technologies for prediction, screening, and detection of cancer Cancer has been classified as a diverse illness with a wide range of subgroups. Its early identification and prognosis, which have become a requirement of cancer research, are essential for clinical treatment. Patients have already benefited greatly from the use of artificial intelligence (AI), machine learning (ML), and deep learning (DL) algorithms in the field of healthcare. AI simulates and combines data, pre-programmed rules, and knowledge to produce predictions. Data are used to improve efficiency across several pursuits and tasks through the art of ML. DL is a larger family of ML methods based on representational learning and simulated neural networks. Support vector machines, convulsion neural networks, and artificial neural networks, among others, have been widely used in cancer research to construct prediction models that enable precise and effective decision-making. Although using these innovative methods can enhance our comprehension of how cancer progresses, further validation is required before these techniques can be used in routine clinical practice. We cover contemporary methods used in the modelling of cancer development in this article. The presented prediction models are built using a variety of guided ML approaches, as well as numerous input attributes and data collections. Early identification and cost-effective detection of cancer's progression are equally necessary for successful treatment of the disease. Smart material-based detection techniques can give end consumers a portable, affordable instrument to easily detect and monitor their health issues without the need for specialized knowledge. Owing to their cost-effectiveness, excellent sensitivity, multimodal detection capacity, and miniaturization aptitude, two-dimensional (2D) materials have a lot of prospects for clinical examination of various compounds as well as cancer biomarkers. The effectiveness of traditional devices is moving faster towards more useful techniques thanks to developments in 2D material-based biosensors/sensors. The most current developments in the design of 2D material-based biosensors/sensors-the next wave of cancer screening instruments-are also outlined in this article. Artificial intelligence ; Computer science ; Machine learning ; Artificial neural network ; Identification (biology) ; Variety (cybernetics) ; Construct (python library) ; Clinical decision support system ; Deep learning ; Decision support system Machine Learning in Materials Science ; COVID-19 diagnosis using AI ; Advanced biosensing and bioanalysis techniques
Authors: Kun Mi | Wei‐Chun Chou | Qiran Chen | Long Yuan | V. Kamineni | Yashas Kuchimanchi | Chunla He | Nancy A. Monteiro‐Riviere | Jim E. Riviere | Zhoumeng Lin
Source: Journal of Controlled Release (article, 2024-08-16)
Field: Materials Science — Biomaterials

Predicting tissue distribution and tumor delivery of nanoparticles in mice using machine learning models Nanoparticles (NPs) can be designed for targeted delivery in cancer nanomedicine, but the challenge is a low delivery efficiency (DE) to the tumor site. Understanding the impact of NPs' physicochemical properties on target tissue distribution and tumor DE can help improve the design of nanomedicines. Multiple machine learning and artificial intelligence models, including linear regression, support vector machine, random forest, gradient boosting, and deep neural networks (DNN), were trained and validated to predict tissue distribution and tumor delivery based on NPs' physicochemical properties and tumor therapeutic strategies with the dataset from Nano-Tumor Database. Compared to other machine learning models, the DNN model had superior predictions of DE to tumors and major tissues. The determination coefficients (R<sup>2</sup>) for the test datasets were 0.41, 0.42, 0.45, 0.79, 0.87, and 0.83 for DE in tumor, heart, liver, spleen, lung, and kidney, respectively. All the R<sup>2</sup> and root mean squared error (RMSE) results of the test datasets were similar to the 5-fold cross validation results. Feature importance analysis showed that the core material of NPs played an important role in output predictions among all physicochemical properties. Furthermore, multiple NP formulations with greater DE to the tumor were determined by the DNN model. To facilitate model applications, the final model was converted to a web dashboard. This model could serve as a high-throughput pre-screening tool to support the design of new and efficient nanomedicines with greater tumor DE and serve as an alternative tool to reduce, refine, and partially replace animal experimentation in cancer nanomedicine research. Nanoparticle ; Distribution (mathematics) ; Computer science ; Artificial intelligence ; Nanotechnology ; Materials science ; Mathematics Nanoparticle-Based Drug Delivery ; Graphene and Nanomaterials Applications ; Nanoplatforms for cancer theranostics
Authors: Ying Wang | Zhijie Jiang | Dong-Rong Wang | Weigang Lu | Dan Li
Source: Journal of the American Chemical Society (article, 2024-02-29)
Field: Chemistry — Inorganic Chemistry

Machine Learning-Assisted Discovery of Propane-Selective Metal–Organic Frameworks Machine learning is gaining momentum in the prediction and discovery of materials for specific applications. Given the abundance of metal-organic frameworks (MOFs), computational screening of the existing MOFs for propane/propylene (C<sub>3</sub>H<sub>8</sub>/C<sub>3</sub>H<sub>6</sub>) separation could be equally important for developing new MOFs. Herein, we report a machine learning-assisted strategy for screening C<sub>3</sub>H<sub>8</sub>-selective MOFs from the CoRE MOF database. Among the four algorithms applied in machine learning, the random forest (RF) algorithm displays the highest degree of accuracy. We experimentally verified the identified top-performing MOF (JNU-90) with its benchmark selectivity and separation performance of directly producing C<sub>3</sub>H<sub>6</sub>. Considering its excellent hydrolytic stability, JNU-90 shows great promise in the energy-efficient separation of C<sub>3</sub>H<sub>8</sub>/C<sub>3</sub>H<sub>6</sub>. This work may accelerate the development of MOFs for challenging separations. Propane ; Chemistry ; Metal-organic framework ; Benchmark (surveying) ; Selectivity ; Work (physics) ; Random forest ; Nanotechnology ; Artificial intelligence ; Computer science ; Organic chemistry ; Mechanical engineering ; Engineering Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; X-ray Diffraction in Crystallography
Authors: Noureddine El Messaoudi | Youssef Miyah | Zeynep Mine Şenol | Zeynep Ciğeroğlu | Emine Sena Kazan-Kaya | Sylwia Gubernat | Jordana Georgin | Dison S.P. Franco
Source: Nano-Structures & Nano-Objects (article, 2024-05-01)
Field: Materials Science — Materials Chemistry

Comprehensive analytical review of heavy metal removal efficiency using agricultural solid waste-based bionanocomposites Municipal solid waste ; Waste management ; Heavy metals ; Metal ; Environmental science ; Materials science ; Environmental chemistry ; Metallurgy ; Chemistry ; Engineering Nanoparticles: synthesis and applications ; Recycling and Waste Management Techniques ; Microplastics and Plastic Pollution
Authors: Ali M. Onaizi | Mugahed Amran | Waiching Tang | Nour Betoush | Mohammad Alhassan | Raizal S.M. Rashid | Mohammad.Fares Yasin | Khaled H. Bayagoob | Sagheer A. Onaizi
Source: Journal of Building Engineering (review, 2024-09-20)
Field: Materials Science — Materials Chemistry

Radiation-shielding concrete: A review of materials, performance, and the impact of radiation on concrete properties The efficiency of radiation shielding is crucial across industries having radioactive activities, from medical facilities to nuclear power stations. Radiation-Shielding Concrete (RSC) emerges as the preferred material for its cost-effectiveness, robust mechanical performance, ease of production, and excellent radiation attenuation properties against ionizing radiations such as gamma rays, X-rays, and neutrons. This comprehensive review delves into the evolution of SCI indexed research on concrete materials for radiation protection, focusing primarily on studies published in the last decade. It meticulously analyze the latest literature to understand how RSC materials enhance radiation attenuation. The review provides valuable insights into the influence of irradiation on both macro- and micro-properties, enriching the knowledge base for material efficiency and effectiveness concerning different types of radiation and shielding requirements. Additionally, this review with a set of recommendations for future research to advance progress in modern construction, encouraging further examination and innovation in the selection of RSC materials. • The effectiveness of various materials in Radiation-Shielding Concrete (RSC) production has been identified and analysed. • The impact of irradiation on the micro and macro properties of concrete has been thoroughly explored. • The mechanisms of interaction between irradiation and cement hydrates, along with their multi-scale effects, have been reviewed. • The influence of irradiation in inducing Alkali-Silica Reaction (ASR) has been explored. • The key future research needs for enhancing RSC have been identified. Radiation shielding ; Electromagnetic shielding ; Radiation ; Materials science ; Composite material ; Forensic engineering ; Structural engineering ; Engineering ; Optics ; Physics Radiation Shielding Materials Analysis ; Graphite, nuclear technology, radiation studies ; Nuclear materials and radiation effects
Authors: Zihao Zhao | Sergiu Clima | Daniele Garbin | R. Degraeve | Geoffrey Pourtois | Zhitang Song | Min Zhu
Source: Nano-Micro Letters (review, 2024-01-11)
Field: Materials Science — Materials Chemistry

Chalcogenide Ovonic Threshold Switching Selector Today's explosion of data urgently requires memory technologies capable of storing large volumes of data in shorter time frames, a feat unattainable with Flash or DRAM. Intel Optane, commonly referred to as three-dimensional phase change memory, stands out as one of the most promising candidates. The Optane with cross-point architecture is constructed through layering a storage element and a selector known as the ovonic threshold switch (OTS). The OTS device, which employs chalcogenide film, has thereby gathered increased attention in recent years. In this paper, we begin by providing a brief introduction to the discovery process of the OTS phenomenon. Subsequently, we summarize the key electrical parameters of OTS devices and delve into recent explorations of OTS materials, which are categorized as Se-based, Te-based, and S-based material systems. Furthermore, we discuss various models for the OTS switching mechanism, including field-induced nucleation model, as well as several carrier injection models. Additionally, we review the progress and innovations in OTS mechanism research. Finally, we highlight the successful application of OTS devices in three-dimensional high-density memory and offer insights into their promising performance and extensive prospects in emerging applications, such as self-selecting memory and neuromorphic computing. Chalcogenide ; Materials science ; Optoelectronics ; Chalcogenide glass Phase-change materials and chalcogenides ; Semiconductor materials and interfaces ; Advanced Memory and Neural Computing
Authors: Zhixiang Fan | Chao Qian | Yuetian Jia | Yiming Feng | Haoliang Qian | Er‐Ping Li | Romain Fleury | Hongsheng Chen
Source: Nature Communications (article, 2024-10-31)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Holographic multiplexing metasurface with twisted diffractive neural network As the cornerstone of AI generated content, data drives human-machine interaction and is essential for developing sophisticated deep learning agents. Nevertheless, the associated data storage poses a formidable challenge from conventional energy-intensive planar storage, high maintenance cost, and the susceptibility to electromagnetic interference. In this work, we introduce the concept of metasurface disk, meta-disk, to expand the capacity limits of optical holographic storage by leveraging uncorrelated structural twist. We develop a physical twisted neural network to describe the optical behavior of the meta-disk and conduct a comprehensive lateral error analysis, where the meta-disk stores large volumes of information through internal structural multiplexing. Two-layer 640 µm x 640 µm meta-disk is sufficient to store over hundreds of high-fidelity images with SSIM of 0.8. By harnessing advanced three-dimensional (3D) printing technology, optical holographic storage is experimentally demonstrated with Pancharatnam-Berry metasurfaces. Our technology provides essential backing for the next generation of optical storage, display, encryption, and multifunctional optical analog computing. Holography ; Multiplexing ; Computer science ; Artificial neural network ; Optics ; Physics ; Artificial intelligence ; Telecommunications Metamaterials and Metasurfaces Applications ; Advanced Optical Imaging Technologies ; Neural Networks and Reservoir Computing