jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Н. А. Печникова | Kalliopi Domvri | Κonstantinos Porpodis | Maria S. Istomina | Aleksandra V. Iaremenko | Alexey V. Yaremenko
Source: Aggregate (article, 2024-11-28)
Field: Materials Science — Materials Chemistry

Carbon Quantum Dots in Biomedical Applications: Advances, Challenges, and Future Prospects ABSTRACT Carbon quantum dots (CQDs) represent a rapidly emerging class of nanomaterials with significant potential in biomedical applications due to their tunable fluorescence, high biocompatibility, and versatile functionalization. This review focuses on the recent progress in utilizing CQDs for drug delivery, bioimaging, biosensing, and cancer therapy. With their unique optical properties, such as tunable fluorescence, high quantum yield, and photostability, CQDs enable precise bioimaging and sensitive biosensing. Their small size, biocompatibility, and ease of surface functionalization allow for the development of targeted drug delivery systems, enhancing therapeutic precision and minimizing side effects. In cancer therapy, CQDs have shown potential in photodynamic and photothermal treatments by generating reactive oxygen species under light exposure, selectively targeting cancer cells while sparing healthy tissues. Furthermore, CQDs’ ability to penetrate biological barriers including the blood–brain barrier opens new possibilities for delivering therapeutic agents to hard‐to‐reach areas, such as tumors or diseased tissues. However, challenges such as optimizing synthesis, ensuring long‐term stability, and addressing safety concerns in biological environments remain critical hurdles. This review discusses current efforts to overcome these barriers and improve CQD performance in clinical settings, including scalable production methods and enhanced biocompatibility. As research progresses, CQDs are expected to play an important role in improving healthcare by offering more targeted treatment options and contributing to advancements in personalized medicine. Quantum dot ; Nanotechnology ; Carbon fibers ; Carbon quantum dots ; Materials science Carbon and Quantum Dots Applications ; Graphene and Nanomaterials Applications ; Nanocluster Synthesis and Applications
Authors: Yeşeren Saylan | Seçkin Kılıç | Adi̇l Deni̇zli̇
Source: Processes (article, 2024-01-12)
Field: Chemistry — Analytical Chemistry

Biosensing Applications of Molecularly Imprinted-Polymer-Based Nanomaterials In the realm of sensing technologies, the appeal of sensors lies in their exceptional detection ability, high selectivity, sensitivity, cost-effectiveness, and minimal sample usage. Notably, molecularly imprinted polymer (MIP)-based sensors have emerged as focal points of interest spanning from clinical to environmental applications. These sensors offer a promising avenue for rapid, selective, reusable, and real-time screening of diverse molecules. The preparation technologies employed in crafting various polymer formats, ranging from microparticles to nanomaterials, wield a profound influence. These techniques significantly impact the assembly of simplified sensing systems, showcasing remarkable compatibility with other technologies. Moreover, they are poised to play a pivotal role in the realization of next-generation platforms, streamlining the fabrication of sensing systems tailored for diverse objectives. This review serves as a comprehensive exploration, offering concise insights into sensors, the molecular imprinting method, and the burgeoning domain of MIP-based sensors along with their applications. Delving into recent progress, this review provides a detailed summary of advances in imprinted-particle- and gel-based sensors, illuminating the creation of novel sensing systems. Additionally, a thorough examination of the distinctive properties of various types of MIP-based sensors across different applications enriches the understanding of their versatility. In the concluding sections, this review highlights the most recent experiments from cutting-edge studies on MIP-based sensors targeting various molecules. By encapsulating the current state of research, this review acts as a valuable resource, offering a snapshot of the dynamic landscape of MIP-based sensor development and its potential impact on diverse scientific and technological domains. Nanotechnology ; Computer science ; Molecularly imprinted polymer ; Systems engineering ; Data science ; Engineering ; Materials science ; Chemistry Analytical chemistry methods development ; Analytical Chemistry and Sensors ; Advanced Chemical Sensor Technologies
Authors: Ashraf Ali | Sanjit Manohar Majhi | Lamia A. Siddig | Abdul Hakeem | Hongli Wen | Naser Qamhieh | Yaser E. Greish | Saleh T. Mahmoud
Source: Biosensors (review, 2024-10-12)
Field: Materials Science — Materials Chemistry

Recent Advancements in MXene-Based Biosensors for Health and Environmental Applications—A Review Owing to their unique physicochemical properties, MXenes have emerged as promising materials for biosensing applications. This review paper comprehensively explores the recent advancements in MXene-based biosensors for health and environmental applications. This review begins with an introduction to MXenes and biosensors, outlining various types of biosensors including electrochemical, enzymatic, optical, and fluorescent-based systems. The synthesis methods and characteristics of MXenes are thoroughly discussed, highlighting the importance of these processes in tailoring MXenes for specific biosensing applications. Particular attention is given to the development of electrochemical MXene-based biosensors, which have shown remarkable sensitivity and selectivity in detecting various analytes. This review then delves into enzymatic MXene-based biosensors, exploring how the integration of MXenes with enzymes enhances sensor performance and expands the range of detectable biomarkers. Optical biosensors based on MXenes are examined, focusing on their mechanisms and applications in both healthcare and environmental monitoring. The potential of fluorescent-based MXene biosensors is also investigated, showcasing their utility in imaging and sensing applications. In addition, MXene-based potential wearable biosensors have been discussed along with the role of MXenes in volatile organic compound (VOC) detection for environmental applications. Finally, this paper concludes with a critical analysis of the current state of MXene-based biosensors and provides insights into future perspectives and challenges in this rapidly evolving field. MXenes ; Biosensor ; Nanotechnology ; Biochemical engineering ; Computer science ; Materials science ; Engineering MXene and MAX Phase Materials ; Advanced biosensing and bioanalysis techniques ; Graphene and Nanomaterials Applications
Authors: C. Femina Carolin | T. Kamalesh
Source: Heliyon (review, 2024-04-19)
Field: Materials Science — Materials Chemistry

Advances in stabilization of metallic nanoparticle with biosurfactants- a review on current trends Recently, research based on new biomaterials for stabilizing metallic nanoparticles has increased due to their greater environmental friendliness and lower health risk. Their stability is often a critical factor influencing their performance and shelf life. Nowadays, the use of biosurfactants is gaining interest due to their sustainable advantages. Biosurfactants are used for various commercial and industrial applications such as food processing, therapeutic applications, agriculture, etc. Biosurfactants create stable coatings surrounding nanoparticles to stop agglomeration and provide long-term stability. The present review study describes a collection of important scientific works on stabilization and capping of metallic nanoparticles as biosurfactants. This review also provides a comprehensive overview of the intrinsic properties and environmental aspects of metal nanoparticles coated with biosurfactants. In addition, future methods and potential solutions for biosurfactant-mediated stabilization in nanoparticle synthesis are also highlighted. The objective of this study is to ensure that the stabilized nanoparticles exhibit biocompatible properties, making them suitable for applications in medicine and biotechnology. Nanotechnology ; Biocompatible material ; Nanoparticle ; Biochemical engineering ; Materials science ; Engineering ; Biomedical engineering Nanoparticles: synthesis and applications ; Environmental remediation with nanomaterials ; Microbial bioremediation and biosurfactants
Authors: Mohammed Majdoub | Dineshkumar Sengottuvelu | Sasan Nouranian | Ahmed Al‐Ostaz
Source: ChemSusChem (article, 2024-03-03)
Field: Materials Science — Materials Chemistry

Graphitic Carbon Nitride Quantum Dots (g‐C<sub>3</sub>N<sub>4</sub> QDs): From Chemistry to Applications Since their emergence in 2014, graphitic carbon nitride quantum dots (g-C<sub>3</sub>N<sub>4</sub> QDs) have attracted much interest from the scientific community due to their distinctive physicochemical features, including structural, morphological, electrochemical, and optoelectronic properties. Owing to their desirable characteristics, such as non-zero band gap, ability to be chemically functionalized or doped, possessing tunable properties, outstanding dispersibility in different media, and biocompatibility, g-C<sub>3</sub>N<sub>4</sub> QDs have shown promise for photocatalysis, energy devices, sensing, bioimaging, solar cells, optoelectronics, among other applications. As these fields are rapidly evolving, it is very strenuous to pinpoint the emerging challenges of the g-C<sub>3</sub>N<sub>4</sub> QDs development and application during the last decade, mainly due to the lack of critical reviews of the innovations in the g-C<sub>3</sub>N<sub>4</sub> QDs synthesis pathways and domains of application. Herein, an extensive survey is conducted on the g-C<sub>3</sub>N<sub>4</sub> QDs synthesis, characterization, and applications. Scenarios for the future development of g-C<sub>3</sub>N<sub>4</sub> QDs and their potential applications are highlighted and discussed in detail. The provided critical section suggests a myriad of opportunities for g-C<sub>3</sub>N<sub>4</sub> QDs, especially for their synthesis and functionalization, where a combination of eco-friendly/single step synthesis and chemical modification may be used to prepare g-C<sub>3</sub>N<sub>4</sub> QDs with, for example, enhanced photoluminescence and production yields. Graphitic carbon nitride ; Nanotechnology ; Quantum dot ; Materials science ; Carbon nitride ; Band gap ; Photocatalysis ; Surface modification ; Photoluminescence ; Biocompatibility ; Characterization (materials science) ; Chemistry ; Catalysis ; Optoelectronics ; Organic chemistry Carbon and Quantum Dots Applications ; Advanced Photocatalysis Techniques ; ZnO doping and properties
Authors: Zhao Ding | Yuting Li | Han Jiang | Yang Zhou | Haiyi Wan | Junqi Qiu | F. Jiang | Jun Tan | Wenjia Du | Yuan Chen | Leon L. Shaw | Fusheng Pan
Source: Interdisciplinary materials (article, 2024-10-16)
Field: Materials Science — Materials Chemistry

The integral role of high‐entropy alloys in advancing solid‐state hydrogen storage Abstract High‐entropy alloys (HEAs) have emerged as a groundbreaking class of materials poised to revolutionize solid‐state hydrogen storage technology. This comprehensive review delves into the intricate interplay between the unique compositional and structural attributes of HEAs and their remarkable hydrogen storage performance. By meticulously exploring the design strategies and synthesis techniques, encompassing experimental procedures, thermodynamic calculations, and machine learning approaches, this work illuminates the vast potential of HEAs in surmounting the challenges faced by conventional hydrogen storage materials. The review underscores the pivotal role of HEAs' diverse elemental landscape and phase dynamics in tailoring their hydrogen storage properties. It elucidates the complex mechanisms governing hydrogen absorption, diffusion, and desorption within these novel alloys, offering insights into enhancing their reversibility, cycling stability, and safety characteristics. Moreover, it highlights the transformative impact of advanced characterization techniques and computational modeling in unraveling the structure–property relationships and guiding the rational design of high‐performance HEAs for hydrogen storage applications. By bridging the gap between fundamental science and practical implementation, this review sets the stage for the development of next‐generation solid‐state hydrogen storage solutions. It identifies key research directions and strategies to accelerate the deployment of HEAs in hydrogen storage systems, including the optimization of synthesis routes, the integration of multiscale characterization, and the harnessing of data‐driven approaches. Ultimately, this comprehensive analysis serves as a roadmap for the scientific community, paving the way for the widespread adoption of HEAs as a disruptive technology in the pursuit of sustainable and efficient hydrogen storage for a clean energy future. Hydrogen storage ; Solid-state ; High entropy alloys ; Materials science ; Hydrogen ; Entropy (arrow of time) ; Thermodynamics ; Statistical physics ; Metallurgy ; Engineering physics ; Physics ; Alloy ; Quantum mechanics Hydrogen Storage and Materials ; High Entropy Alloys Studies ; High-Temperature Coating Behaviors
Authors: Jeremy A. McCulloch | Skyler R. St. Pierre | Kevin Linka | Ellen Kuhl
Source: International Journal for Numerical Methods in Engineering (article, 2024-04-08)
Field: Materials Science — Materials Chemistry

On sparse regression, <i>L</i><sub><i>p</i></sub>‐regularization, and automated model discovery Abstract Sparse regression and feature extraction are the cornerstones of knowledge discovery from massive data. Their goal is to discover interpretable and predictive models that provide simple relationships among scientific variables. While the statistical tools for model discovery are well established in the context of linear regression, their generalization to nonlinear regression in material modeling is highly problem‐specific and insufficiently understood. Here we explore the potential of neural networks for automatic model discovery and induce sparsity by a hybrid approach that combines two strategies: regularization and physical constraints. We integrate the concept of L p regularization for subset selection with constitutive neural networks that leverage our domain knowledge in kinematics and thermodynamics. We train our networks with both, synthetic and real data, and perform several thousand discovery runs to infer common guidelines and trends: L 2 regularization or ridge regression is unsuitable for model discovery; L 1 regularization or lasso promotes sparsity, but induces strong bias that may aggressively change the results; only L 0 regularization allows us to transparently fine‐tune the trade‐off between interpretability and predictability, simplicity and accuracy, and bias and variance. With these insights, we demonstrate that L p regularized constitutive neural networks can simultaneously discover both, interpretable models and physically meaningful parameters. We anticipate that our findings will generalize to alternative discovery techniques such as sparse and symbolic regression, and to other domains such as biology, chemistry, or medicine. Our ability to automatically discover material models from data could have tremendous applications in generative material design and open new opportunities to manipulate matter, alter properties of existing materials, and discover new materials with user‐defined properties. Interpretability ; Leverage (statistics) ; Computer science ; Regularization (linguistics) ; Machine learning ; Regression ; Artificial intelligence ; Knowledge extraction ; Artificial neural network ; Data mining ; Mathematics ; Statistics Machine Learning in Materials Science ; Probabilistic and Robust Engineering Design ; Composite Material Mechanics
Authors: Michael A. Skinnider
Source: Nature Machine Intelligence (article, 2024-03-29)
Field: Materials Science — Materials Chemistry

Invalid SMILES are beneficial rather than detrimental to chemical language models Abstract Generative machine learning models have attracted intense interest for their ability to sample novel molecules with desired chemical or biological properties. Among these, language models trained on SMILES (Simplified Molecular-Input Line-Entry System) representations have been subject to the most extensive experimental validation and have been widely adopted. However, these models have what is perceived to be a major limitation: some fraction of the SMILES strings that they generate are invalid, meaning that they cannot be decoded to a chemical structure. This perceived shortcoming has motivated a remarkably broad spectrum of work designed to mitigate the generation of invalid SMILES or correct them post hoc. Here I provide causal evidence that the ability to produce invalid outputs is not harmful but is instead beneficial to chemical language models. I show that the generation of invalid outputs provides a self-corrective mechanism that filters low-likelihood samples from the language model output. Conversely, enforcing valid outputs produces structural biases in the generated molecules, impairing distribution learning and limiting generalization to unseen chemical space. Together, these results refute the prevailing assumption that invalid SMILES are a shortcoming of chemical language models and reframe them as a feature, not a bug. Computer science ; Risk analysis (engineering) ; Intensive care medicine ; Medicine Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Machine Learning in Bioinformatics
Authors: Xiaohu Ge | Jun Yin | Zhouhong Ren | Kelin Yan | Yundao Jing | Yueqiang Cao | Nina Fei | Xi Liu | Xiaonan Wang | Xinggui Zhou | Liwei Chen | Weikang Yuan | Xuezhi Duan
Source: Journal of the American Chemical Society (article, 2024-02-09)
Field: Materials Science — Materials Chemistry

Atomic Design of Alkyne Semihydrogenation Catalysts via Active Learning Alkyne hydrogenation on palladium-based catalysts modified with silver is currently used in industry to eliminate trace amounts of alkynes in alkenes produced from steam cracking and alkane dehydrogenation processes. Intensive efforts have been devoted to designing an alternative catalyst for improvement, especially in terms of selectivity and catalyst cost, which is still far away from that as expected. Here, we describe an atomic design of a high-performance Ni-based intermetallic catalyst aided by active machine learning combined with density functional theory calculations. The engineered NiIn catalyst exhibits >97% selectivity to ethylene and propylene at the full conversion of acetylene and propyne at mild temperature, outperforming the reported Ni-based catalysts and even noble Pd-based ones. Detailed mechanistic studies using theoretical calculations and advanced characterizations elucidate that the atomic-level defined coordination environment of Ni sites and well-designed hybridization of Ni 3<i>d</i> with In 5<i>p</i> orbital determine the semihydrogenation pathway. Propyne ; Catalysis ; Dehydrogenation ; Alkyne ; Chemistry ; Acetylene ; Selectivity ; Intermetallic ; Density functional theory ; Cracking ; Ethylene ; Alkane ; Chemical engineering ; Combinatorial chemistry ; Nanotechnology ; Organic chemistry ; Computational chemistry ; Materials science Machine Learning in Materials Science ; Catalytic Processes in Materials Science ; Catalysis and Oxidation Reactions
Authors: Atul D. Watpade | Sanketsinh Thakor | Prince Jain | Prajna P. Mohapatra | Chandan R. Vaja | Anand Joshi | Dimple Shah | Mohammad Tariqul Islam
Source: Ain Shams Engineering Journal (article, 2024-03-14)
Field: Materials Science — Materials Chemistry

Comparative analysis of machine learning models for predicting dielectric properties in MoS2 nanofiller-reinforced epoxy composites This research investigates the dielectric properties of nano epoxy composites by incorporating various concentrations of MoS2 into epoxy resin. The study explores the impact of synthesized nanoparticles on undoped epoxy composites, specifically focusing on their potential applications in dielectric materials. The experimental synthesis and characterization of nanoepoxy composites typically involve time-consuming and expensive methods. This study compares five machine learning (ML) models—random forests, decision trees, extra trees, XGBoost, and gradient boosting—in order to predict the frequency-dependent dielectric constants in these composites under different nanofiller variations in order to address this challenge. To ensure robust model performance, training is carried out on different subsets of the dataset, ranging from 60% to 30%, while the remaining portions are reserved for testing purposes (40% to 70%). The main objective of the study is to assess the performance of each regressor technique using metrics such as adjusted R2 score, MSE, RMSE, and MAE, in which the ET regressor excels. The ET method demonstrates exceptional performance, achieving an adjusted R2 value of 0.9977 and 0.9912 for target variables ε′ and ε′′, respectively when tested with a size of 0.4. The findings underscore the potential of ML models for precise and efficient prediction of frequency-dependent dielectric constants of nanoepoxy composites with various concentrations of nanofillers, offering an alternative to time-consuming and expensive laboratory work. Epoxy ; Dielectric ; Materials science ; Composite material ; Boosting (machine learning) ; Random forest ; Nanoparticle ; Gradient boosting ; Machine learning ; Computer science ; Nanotechnology High voltage insulation and dielectric phenomena ; Smart Materials for Construction ; Advanced Sensor and Energy Harvesting Materials
Authors: Tatiana Latychevskaia | D. A. Bandurin | Kostya S. Novoselov
Source: Nature Reviews Physics (review, 2024-06-17)
Field: Materials Science — Materials Chemistry

A new family of septuple-layer 2D materials of MoSi2N4-like crystals Layer (electronics) ; Materials science ; Nanotechnology MXene and MAX Phase Materials ; 2D Materials and Applications ; Advanced Photocatalysis Techniques
Authors: Lenka Lorencová | Peter Kasák | Natália Košútová | Monika Jerigová | Eva Noskovičová | Alica Vikartovská | Marek Baráth | Pavol Farkaš | Jan Tkáč
Source: Microchimica Acta (review, 2024-01-11)
Field: Materials Science — Materials Chemistry

MXene-based electrochemical devices applied for healthcare applications The initial part of the review provides an extensive overview about MXenes as novel and exciting 2D nanomaterials describing their basic physico-chemical features, methods of their synthesis, and possible interfacial modifications and techniques, which could be applied to the characterization of MXenes. Unique physico-chemical parameters of MXenes make them attractive for many practical applications, which are shortly discussed. Use of MXenes for healthcare applications is a hot scientific discipline which is discussed in detail. The article focuses on determination of low molecular weight analytes (metabolites), high molecular weight analytes (DNA/RNA and proteins), or even cells, exosomes, and viruses detected using electrochemical sensors and biosensors. Separate chapters are provided to show the potential of MXene-based devices for determination of cancer biomarkers and as wearable sensors and biosensors for monitoring of a wide range of human activities. Health care ; Computer science ; Electrochemistry ; Materials science ; Physics ; Electrode ; Political science MXene and MAX Phase Materials ; Advanced Memory and Neural Computing ; Energy Harvesting in Wireless Networks