Materials Science Research Research Articles

Unveiling the secrets of unusual long-pitch periodic assembly of poly(ʟ-Lactide) ring-banded spherulites

Although polymer physicists have conducted extensive research over the decades, the molecular assembly in lamellae of ring-banded spherulites (RBS) remains a puzzle that has yet to be solved. Nonetheless, a breakthrough has occurred in this field where long-pitch banded rings made of a model poly(ʟ-lactide) (PLLA) were examined using advanced synchrotron X-ray micro-focused diffraction techniques. Through the microbeam diffraction mapping and microscopy 3D dissection morphology, the periodic banded structure of RBS is composed of lamellae that self-assemble as structural gratings. Furthermore, a comparison of optical micrograph image with the mapping results revealed the sequential arrangement and orientation of lamellae in these gratings. These extensive experimental findings now provide a comprehensive and novel explanation for the formation of long-pitch lamellar assembly in RBS. This new methodology is adaptable to a wide variety of polymers and allows for the analysis of spherulites displaying periodically self-assembled patterns. The novel methodology employed here has a versatile applicability for a diverse range of polymers. This technique can be extended to analyze spherulites showcasing periodically self-assembled patterns. Such revelation provides valuable understanding into the complex structures and behaviors of polymers, and this discovery opens up new frontiers in materials science research and its practical applications.

(Invited) Application of 3D in-Situ X-Ray Visualization to Track the Formation of Dislocation Clusters during PVT Growth of SiC

SiC has become the key player among wide bandgap semiconductors for power electronic applications. Since the first description of the physical vapour transport (PVT) growth process of SiC by Tairov and Tsvetkov (J. Crystal Growth, 43, 209(1978)), there has been steady progress in SiC-based crystal growth, epitaxy and device processing. The success of SiC compared to Si is related to its superior material properties such as extremely high electrical breakdown field and high thermal conductivity compared to the standard silicon counterpart. In addition, SiC device processing utilises much of the standard Si processing equipment. A major reason for the success of SiC in power electronic applications compared to other wide bandgap counterparts such as GaN, Ga2O3 and diamond is related to the availability of large diameter SiC wafer materials (150mm = standard, 200mm = developped). Bulk SiC growth is now a very well developed process with comparatively high yields. The extraordinary physical properties also include obstacles related to the strong chemical bonding and complex phase diagram of the material, which pose challenges to the growth process. Therefore, there are still a number of open questions related to the nucleation, progression and termination of the bulk growth process that require fundamental research in materials science and technology.The aim of this presentation is (i) to give an overview of the state-of-the-art PVT growth process and (ii) to discuss a current research topic dealing with the early stage of the growth process and the defect formation that can occur during the initial nucleation of SiC. We have applied 3D in-situ visualisation of the growth process using X-ray computed tomography to visualise island formation on the large seeding area. These data are related to growth process instabilities such as temperature variations during the seeding process and axial doping level changes from the seed to the newly grown crystal. Both process instabilities induce mechanical stress on the SiC lattice and act as sources for dislocation generation and multiplication. We will show a series of growth processes with varying growth parameters that shed light on the initial growth stage of SiC.As the crystal diameter of SiC increases from 150 mm to 200 mm, the results of this study become increasingly important.

Mixed Ionic Electronic Conduction Caused by Phase Transformation and Interfacial Segregation in an Entropy Stabilized Oxide

Research on entropy-stabilized oxides (ESO) [1] has primarily focused on exploring new single-phase structures with diverse chemistries, or unique properties, such as high Li-ion conductivity, high dielectric constant, low thermal conductivity, good electrochemical cycling stability, and high storage capacity retention [2]. However, few studies discuss the impact of secondary phase formation on functionality such as electrical conductivity [3] despite the fact that annealing this single-phase ESO causes it to undergo a phase transformation leading to the formation of Cu-rich and Co-rich secondary phases [4]. To address this gap, this work [5] investigates the electrical transport mechanisms in the canonical entropy-stabilized oxide (Co,Cu,Mg,Ni,Zn)O as a function of secondary phase content. Here, the structure and composition of single-phase and multiphase ESOs, and their GBs and HIs are probed using atomic-resolution imaging by scanning transmission electron microscopy (STEM) coupled with nanoscale spectroscopy by electron energy-dispersive spectroscopy (EDS) and electron energy-loss spectroscopy (EELS). Our findings suggest that when single-phase, the oxide is an oxygen ion conductor. After 2 h of heat treatment, Cu-rich tenorite particles form at some grain boundaries, turning the grain interior rocksalt oxide into a mixed ionic-electronic conductor via oxygen ions and small hole polaron Co2+/Co3+ and Cu2+/Cu+ pairs. 24 h of heat treatment leads to the full coverage of Cu-rich tenorite particles at all grain boundaries and the formation of anisotropic Cu-rich tenorite and equiaxed Co-rich spinel particles in the grains. While the grain interiors exhibit similar mixed ionic-electronic conduction, Cu-rich tenorite grain boundary phases create a pathway for Cu2+/Cu3+ small hole polarons. The ability to selectively grow secondary phases nucleated at grain boundaries enables tuning of electrical properties in entropy-stabilized and complex concentrated oxides using microstructure design, nanoscale engineering, and heat treatment, paving the way to develop many novel materials.[1] C. M. Rost, J.-P. Maria, Nat Commun, 2015.[2] A. Salian, S. Mandal, Critical Reviews in Solid State and Materials Sciences, 2022.[3] M. Moździerz, K. Świerczek, Acta Materialia, 2021.[4] A. D. Dupuy, J. M. Schoenung, Journal of the European Ceramic Society, 2021.[5] H. Vahidi...W. J. Bowman (submitted). Acknowledgments This research was primarily supported by the National Science Foundation Materials Research Science and Engineering Center program through the UC Irvine Center for Complex and Active Materials (DMR-2011967). WJB and HV acknowledge partial support from the UCI new faculty startup funding. The authors acknowledge the use of facilities and instrumentation at the UC Irvine Materials Research Institute (IMRI) supported in part by the National Science Foundation Materials Research Science and Engineering Center program through the UC Irvine Center for Complex and Active Materials (DMR-2011967).

Which Interfaces Matter Most? Variability in Grain Boundary Defect Chemistry and Conductivity in a Concentrated Solid Electrolyte

Inorganic solid electrolytes with fast ionic conductivity are critical components of fuel/electrolysis cells, all-solid-state batteries, membrane and separation reactors, sensors and other electrochemical devices. For example, solid oxygen ion (O2-) conductors are used in solid oxide fuel/electrolysis cells (SOFCs/SOECs) for high-efficiency conversion between chemical and electrical energy and chemical production from CO2. One major challenge in engineering such materials is the diminished grain boundary (GB) conductivity in polycrystalline oxide ceramics. This phenomenon, which was initially accredited to highly resistive silica-based glassy impurity phases, is attributed to the formation of intrinsic space charge layers (SCLs) directly adjacent to GB cores. The positive potential at the GB core (due to large concentrations of oxygen vacancies) results in a depletion of oxygen vacancies in the space charge layer as well as the segregation of charge-compensating acceptor solutes from grain interiors which ultimately reduces GB conductivity. Several strategies have been explored to improve GB conductivity by understanding and mitigating the SCL effects. While the standard Poisson–Boltzmann approach models the near-grain boundary defect behavior in dilute solid solutions and recent models by Mebane et al [1] and Virkant et al [2] incorporate the effects of defect-defect interactions (in concentrated solid solutions) and chemomechanical stress, respectively, there is yet to be a model developed that can predict individual GB conductivities in concentrated solid solutions based on direct experimental measurements of near-GB defect chemistry.Here, we present an experimental-computational framework that predicts which GBs in a polycrystalline electrolyte likely facilitate ionic conductivity. We developed a thermodynamic phase-field modeling framework and applied it to a model oxygen electrolyte Gd0.25Ce0.75O2-δ, wherein the GB-to-GB variability in defect concentrations (Gd3+ solutes, electrons localized at Ce3+, and oxygen vacancies) was measured microscopically using scanning transmission electron microscopy electron energy-loss spectroscopy (STEM EELS). These data were used to predict the GB-to-GB variability in cross-GB ionic conduction using a unique model, which prioritizes reproducing microscopically observed GB defect concentrations and considers defect-defect interactions in highly concentrated solid solutions, making the framework applicable to other technologically relevant solid electrolytes. Across the GBs studied, we revealed a non-monotonic relationship between depletion and GB conductivity, with the highest conductivity predicted for intermediate depletion amounts. This work lays the foundation for future experimental-computational research and advanced data-driven design of solid electrolytes needed for functional and energy storage/conversion devices.[1] D. S. Mebane, R. A. D. Souza, Energy & Environmental Science, 2015.[2] K. S. N. Vikrant, R. Edwin García, Energy & Environmental Science, 2018.[3] H. Vahidi, W.J. Bowman (Submitted). Acknowledgments HV and WJB acknowledge primary support from the UCI new faculty startup funding. This research was partially supported by the National Science Foundation Materials Research Science and Engineering Center program through the UC Irvine Center for Complex and Active Materials (DMR-2011967). We acknowledge the use of facilities and instrumentation at the UC Irvine Materials Research Institute (IMRI) supported in part by the same NSF MRSEC (DMR-2011967). AM, DM, and AC acknowledge funding from the NSF [WB1] [hv2] and the WVU MAE research department for the use of the Thorny Flat HPC Cluster for the numerical modeling work and computational resources.

Atomic Force Microscopy Characterization of Electrochemically Polished Nickel-Titanium Memory Alloy

Atomic force microscopy (AFM) is a powerful characterization technique used for monitoring metals or metal alloys surfaces changes [1-14]. AFM works by using a small probe, typically with a sharp tip, to scan the surface of a sample. As the probe moves over the surface, it measures the height and other properties of the surface topography, allowing for detailed analysis of the surface characteristics. Using AFM, it is possible to obtain high-resolution images in 2-dimintinal (2-D) or 3-dimintinal (3-D), allowing for detailed analysis of the surface morphology, roughness, and other properties. AFM can also be used to measure surface features, such as the height and depth of surface defects, or the thickness of surface coatings.Smooth surfaces of metals or metal alloys are needed for many biomedical applications such as implants and stents. Electrochemical polishing (EP) is a process used to smooth and improve the surface finish of metals or alloys by selectively removing surface roughness using an optimum electric current. The resulting surface can have a highly polished and uniform finish. In this study, AFM is used to monitor the surface changed before and after EP of memory alloy (Nickel-Titanium, Ni-Ti Alloy). AFM image of a Ni-Ti Alloy’s surface is polished using a current density ranging from 0 to 0.147 A/cm2, which shows a highly polished surface after for 15 minutes. Surface roughness of the Ni-Ti alloy in ionic liquid as an acid free EP resulted in surface roughness significantly is reduced from 64 nm (before EP) to 11 nm without significantly altering the alloy elemental composition. This study provides an electrochemical acid free EP method to effectively control the relative smoothness of Ni-Ti implants at the nanoscale (less than 12 nm) which could offer many potential biomedical applications including controlling cell interactions with Ni-Ti alloy at bio-interfaces. Overall, AFM is a powerful tool for characterizing metal surfaces after electrochemical polishing, providing detailed information about the surface morphology and chemical composition that can be useful for a range of applications, from materials science research to quality control in manufacturing. References Mahapatro, T. D. Matos Negrón, C. Bonner, and T. M. Abdel-Fattah, J. Biomater. Tissue Eng., 3, 196-204 (2013).Bhure, A. Mahapatro, C. Bonner, T. M. Abdel-Fattah, Materials Science and Engineering: C, 33(4), 2050-2058 (2013).Bhure, T. M. Abdel-Fattah, C. Bonner, J. C. Hall, and A. Mahapatro, Applied Surface Science, 257(13), 5605-5612 (2011).M. Abdel-Fattah, D. Loftis and A.Mahapatro, Journal of Biomedical Nanotechnology, 7(6) 794-800 (2011).M. Abdel-Fattah, D. Loftis and A. Mahapatro, Journal of Surfaces and Interfaces of Materials, 3, 67-74 (2015)JD Loftis, TM Abdel-Fattah, Colloids and Surfaces A: Physicochemical and Engineering Aspects 511, 113-119 (2016)M. Abdel-Fattah, D. Loftis and A. Mahapatro, Nanoscience and Nanotechnology, 5(2), 36-44 (2015)Bhure, T. M. Abdel-Fattah, C. Bonner1, J. C. Hall, and A. Mahapatro, Journal of Biomedical Nanotechnology, 6(2), 117-128 (2010).M. Abdel-Fattah, D. Loftis, ECS Transactions, 25, (39), 57-61 (2010).M. Abdel-Fattah, D. Loftis, A. Mahapatro, ECS Transactions, 25(19), 57-61 (2010).M. Abdel-Fattah, Derek Loftis, ECS Transactions, 25(7), 327-332 (2009).AI Wixtrom, JE Buhler, CE Reece, TM Abdel-Fattah, Journal of The Electrochemical Society 160 (3), E22 (2013)AI Wixtrom, JE Buhler, CE Reece, TM Abdel-Fattah, Journal of Environmental Chemical Engineering 1 (1-2), 18-22 (2013)M. Abdel-Fattah and J, D. Loftis, Sustainable Chemistry, 3(2), 238-247 (2022)M. Abdel-Fattah and J. D. Loftis, Molecules, 25, 23, 5712 (2020)

Gamma-irradiation synthesis of Pd nanoparticles decorated on Bi2WO6 photocatalysts for efficient NOx removal

Visible-light active photocatalysis based on Bi2WO6 nanomaterials towards NOx removal, is a topic that attracts a lot of attention from material science and chemical engineering researchers. In this study, Pd/Bi2WO6 was synthesized by mixing gamma-rays-irradiated Pd2+ solution with Bi2WO6 powder under room temperature conditions. The synthesized materials were characterized using XRD, FT-IR, SEM-EDX, TEM, DRS, and PL techniques. The results demonstrate that Pd/Bi2WO6 exhibits higher efficiency (46.51 %) in degrading NO compared to pure Bi2WO6 (34.46 %). Additionally, the trapping test confirms the significant involvement of electrons and holes in the degradation of NO. The recyclability of Pd/Bi2WO6-based materials was also investigated, as they maintained their structural integrity as well as catalytic activity after five cycles, suggesting their potential for practical applications.

Sustainable production of copper components using concentrated solar energy in material extrusion additive manufacturing (MEX-CSE)

Material extrusion is a suitable technology for the additive manufacturing of complex components from any family of materials. The processing of metallic parts involves a three-stage methodology known as PDS (Printing-Debinding-Sintering). In the printing stage, filaments made of metallic powder and a polymeric binder system are used. After printing, the binder must be removed, and the parts sintered to obtain densified metal components with the final properties. These last two stages, in particular sintering, require high temperatures, and are thus high-energy demanding processes. The use of the Concentrated Solar Energy (CSE) is increasingly the focus of research in materials science as it is a clean, non-polluting, renewable energy resource, which is highly efficient for high temperature materials processing. This is the first study to analyse the feasibility of using CSE in the thermal debinding and sintering stages in the production of metallic components via Printing-Debinding-Solar Sintering (PDSS) technology. The objective is to develop a new sustainable process for producing metallic components by combining additive manufacturing and solar energy. In this study, pure copper cylindrical parts were produced and sintered in a low-cost Fresnel lens. The results revealed that solar sintering occurred at lower temperature (975 °C) and much shorter time (∼1 h) than in the conventional process, enhancing the economic and environmental efficiency of the conventional manufacturing process.

Empowering research in chemistry and materials science through intelligent algorithms

In this review, we explore the integration of intelligent algorithms in chemistry and materials science.We begin by delineating the core principles of Machine Learning, Deep Learning, and optimization algorithms, highlighting their bespoke adaptation to these scientific domains. The focus then shifts to the critical processes of data management, including collection, refinement, and feature engineering, alongside strategies for efficient data mining from targeted databases and literatures. Subsequently, we present a concise overview of the diverse applications of these algorithms, emphasizing their transformative impact in both fields. Finally, this review explores the future prospects and challenges of these emerging algorithms.

Vacuum-free femtosecond fiber laser microplasma X-ray source for radiography.

Radiographic imaging using X-rays is a tool for basic research and applications in industry, materials science, and medical diagnostics. In this article, we present a novel approach for the generation of X-rays using a vacuum-free microplasma by femtosecond fiber laser. By tightly focusing a laser pulse onto a micrometer-sized solid density near-surface plasma from a rotating copper target, we demonstrate the generation of Cu K-photons (8-9 keV) with high yield ∼ 1.6 × 109 phot/s/2π, and with a source size diameter of approximately 10 microns. Femtosecond fiber laser allows working with a high repetition rate (∼2 MHz) and moderate energy levels (10-40 µJ), ensuring the effective quasi-continuous generation of X-ray photons. Furthermore, we introduce a hybrid scheme that combines the tightly focusing laser-plasma X-ray generator with an online control unit for microplasma size source based on the back-reflected second harmonic generated in the laser-induced microplasma. The compactness and high performance of this vacuum-free femtosecond fiber laser microplasma X-ray source makes it a promising solution for advanced radiographic applications. Our preliminary results on the creation of a microfocus X-ray source provide insights into the feasibility and potential of this innovative approach.

Quantitative Topic Analysis of Materials Science Literature Using Natural Language Processing.

Materials science research has garnered extensive attention from industry, society, policy, and academia. However, understanding the research landscape and extracting strategic insights are challenging due to the increasing diversity and volume of publications. This study proposes a natural language processing-based protocol for extracting text-encoded topics from a large volume of scientific literature, uncovering research interests of scientific communities, as well as convergence trends. We report a topic map, representing the materials science research landscape with text-mined 257 topics regarding biocompatible materials, structural materials, electrochemistry, or photonics. We analyze the topic map in terms of national research interests in materials science, revealing competitive positions and strategies of active nations. For example, it is found that the increasing trend of research interest in machine learning topic was captured in the United States earlier than other nations. Similarly, our journal-level analyses serve as reference information for journal recommendations and trend guidance, showing that the main topics and research interests of materials science journals slightly changed over time. Moreover, we build the topic association network which can highlight the status and future potential of interdisciplinary research, revealing research fields with high centrality in the network such as machine learning-enabled composite modeling, energy policy, or wearable electronics. This study offers insightful results on current and near-future materials science research landscapes, facilitating the understanding of stakeholders, amidst the fast-evolving and diverse knowledge of materials science.

Externally Heated Diamond ANvil Cell Experimentation (EH-DANCE) for studying materials and processes under extreme conditions.

Externally heated diamond anvil cells provide a stable and uniform thermal environment, making them a versatile device to simultaneously generatehigh-pressure and high-temperature conditions in various fields of research, such as condensed matter physics, materials science, chemistry, and geosciences. The present study features the Externally Heated Diamond ANvil Cell Experimentation (EH-DANCE) system, a versatile configuration consisting of a diamond anvil cell with a customized microheater for stable resistive heating, bidirectional pressure control facilitated by compression and decompression membranes, and a water-cooled enclosure suitable for vacuum and controlled atmospheres. This integrated system excels with its precise control of both pressure and temperature for mineral and materials science research under extreme conditions. We showcase the capabilities of the system through its successful application in the investigation of the melting temperature and thermal equation of state of high-pressure ice-VII at temperatures up to 1400K. The system was also used to measure the elastic properties of solid ice-VII and liquid H2O using Brillouin scattering and Raman spectra of carbonates using Raman spectroscopy, highlighting the potential of the EH-DANCE system in high-pressure research.

Preface

It is a matter of great pleasure for the organizers to bring out the Conference proceedings of the 6th National Conference on Advanced Materials and Radiation Physics (AMRP-2023) in Journal of Physics: Conference Series. The 6th National Conference on Advanced Materials and Radiation Physics (AMRP-2023) was held during May 18-19, 2023 through HYBRID Mode at Sant Longowal Institute of Engineering and Technology, Longowal, Punjab (INDIA). The AMRP-2023 conference is an important event that brings academics and researchers from all over India to share their professional experiences, expand their professional knowledge and receive updates on the latest advances in the field of advanced materials and radiation Physics.Materials science is an interdisciplinary field which deals with the discovery and design of new materials involving the study of materials through the materials paradigm (synthesis, structure, properties and performance). The subject covers a broad spectrum of materials science research including functional materials, synthesis and processing, theoretical analyses, characterization and properties of materials. Emphasis is also placed on the interdisciplinary nature of materials science and issues at the forefront of the field, such as energy and environmental issues, as well as medical and bioengineering applications are also discussed.The Radiation Physics is highly multidisciplinary in nature and is now the backbone of the nuclear medical diagnostics. The radiation physics research covers many areas of radiation imaging techniques, fundamental studies of semiconductor radiation detectors, detector materials, radiation interaction with matter and medical physics which aims to improve knowledge and methods in the field of radiation physics and radiation dosimetry, in order to develop new radiation measurement instruments and methods for applications in research, industry and medicine.In this conference more than 150 researchers from reputed universities/institutes have shown interest in hybrid mode participation. The conference included one key note address, four plenary talks, six invited talks, and presentation of more than 130 contributed papers via online & offline mode on different aspects of the conference theme. We extend our sincere gratitude to all the authors who submitted their papers to the AMRP conference. We also like to express our great appreciation to the Keynote, plenary and invited speakers.We are thankful to Institute administration for extending all support and motivation to organize this conference. On behalf of the Conference Organizing Committee, we would like to thank the referees for their efficient and thoughtful actions. We are grateful to the members of the Organising Committee for their efforts in making and shaping the program for AMRP 2023. Particularly, we acknowledge the publishing support from the members of Journal of Physics: Conference Series. We hope that the future AMRP will be as successful and stimulating, as indicated with the contributions presented in this volume.Organising Committee AMRP-2023List of Organising Committee is available in this Pdf.

Enhancing SEM positioning precision with a LEGO®-based sample fitting system.

Scanning electron microscopy (SEM) is a precious tool in materials science and morphology sciences, enabling detailed examination of materials at the nanoscale. However, precise and accurate sample repositioning during different observation sessions remains a significant challenge, impacting the quality and repeatability of SEM analyses. This study aimed to develop and evaluate a LEGO®-based sample positioning system for SEM analysis. The system was designed to consistently identify and align features across multiple repositioning cycles, maintain accurate positioning along the z-axis, minimize distortion, and provide repeatable and reliable results. The results indicated a high degree of precision and accuracy in the repositioning process, as evidenced by the minimal displacements, deviations in scaling and shearing, and the highly significant results (p < 0.001) obtained from the analysis of absolute translations and rotations. Moreover, the analyses were consistently replicated across six repetitions, underscoring the reliability of the observed results. While the findings suggest that the LEGO-based sample positioning system is promising for enhancing SEM analyses' quality and repeatability, further studies are needed to optimize the system's design and evaluate its performance in different SEM applications. Ultimately, this study contributes to the ongoing efforts to develop cost-effective, customizable, and accurate solutions for sample positioning in SEM, contributing to the advancement of materials science research and all SEM analysis requiring overtime observations of the same sample. RESEARCH HIGHLIGHTS: This study focused on the development and evaluation of a novel LEGO-based sample positioning system specifically designed for SEM analysis. One of the standout features of this system is its ability to consistently identify and align features across multiple repositioning cycles, showcasing its precision and reliability. To further understand the mechanical aspects of the SEM stage, we employed the Rambold Kontroll comparator, which provided a baseline understanding of its mechanical tolerance. The registration process results were particularly noteworthy, as they revealed high accuracy with minimal displacements. Furthermore, the consistent outcomes observed across multiple repetitions emphasize the reliability and robustness of the methods we employed in this research.

Thrombin-Free Fibrillogenesis and Gelation of Fibrinogen Triggered by Magnesium Sulfate.

Self-assembly of the blood protein fibrinogen is a highly relevant topic in materials science and medical research. This originates from fibrinogen's beneficial material properties such as cell interaction and biocompatibility. Within recent decades, several enzyme-free strategies to create fibers and hydrogels out of fibrinogen have been presented, broadening the spectrum of fibrinogen-based material enormously. Herein, we describe a further method to obtain such a material by adding specifically MgSO4 to fibrinogen. The key of this material is the combination of Mg2+ and a kosmotropic anion, for example sulfate or (hydrogen)phosphate. This effect is most likely related to occupancy of fibrinogen's well-known binding sites for Mg2+, resulting in a significant increase in fiber yield and gel stability. Here, we shine light on the question of how electrostatic interactions via Mg2+ enhance fibrillogenesis and the gelation of fibrinogen and discuss first insights into the material's properties.

Application and development prospects of nanomaterials in multiple fields

Nanomaterials have unique structures and characteristics that allow them to exhibit far superior or new properties in many aspects than conventional materials. With the continuous progress in materials science research, the manufacturing and processing technologies of nanomaterials have also been fully developed in recent years. Nanomaterials of different types, structures, and compositions have been applied in many fields, such as construction, energy, environmental protection, and electronics, providing new ideas for solving many problems. In this paper, we first review the basic definition and properties of nanomaterials, then analyze and discuss the cases of nanomaterials already in use or promising design solutions in medicine, solar energy and textiles. This paper also summarize their advantages and manufacturing difficulties. All of these areas have mature nanomaterial products in use, and they provide ideas for further research and development in the future.

Optimizing Perovskite Thin‐Film Parameter Spaces with Machine Learning‐Guided Robotic Platform for High‐Performance Perovskite Solar Cells

AbstractSimultaneously optimizing the processing parameters of functional thin films remains a challenge. The design and utilization of a fully automated platform called SPINBOT is presented for the engineering of solution‐processed functional thin films. The SPINBOT is capable of performing experiments with high sampling variability through the unsupervised processing of hundreds of substrates with exceptional experimental control. Through the iterative optimization process enabled by the Bayesian optimization (BO) algorithm, the SPINBOT explores an intricate parameter space, continuously improving the quality and reproducibility of the produced thin films. This machine learning (ML)‐guided reliable SPINBOT platform enables the acceleration of the optimization process of perovskite solar cells via a simple photoluminescence characterization of films. As a result, this study arrives at an optimal film that, when processed into a solar cell in an ambient atmosphere, immediately yields a champion power conversion efficiency (PCE) of 21.6% with satisfactory performance reproducibility. The unsealed devices retain 90% of their initial efficiency after 1100 h of continuous operation at 60–65 °C under metal‐halide lamps. It is anticipated that the integration of robotic platforms with the intelligent algorithm will facilitate the widespread adoption of effective autonomous experimentation to address the evolving needs and constraints within the materials science research community.

Physical, chemical, electronic and optical properties of CuO/NiO@n-Si thin films by drop casting method for p–n junction diode

The development of photodetector technology is the outcome of diverse research in materials science, device engineering, and manufacturing methods. Hence, we synthesized p-CuO@n-Si, p-NiO@n-Si, p-CuO/NiO@n-Si and p-CuO/NiO-TX-100@n-Si Schottky Barrier diodes by drop-casting method and characterized by XRD, SEM, EDX, TEM, XPS and UV–visible. From the XRD analysis, the pure p-CuO@n-Si and p-NiO@n-Si, found to exhibit monoclinic and cubic crystal structure respectively whereas p-CuO/NiO@n-Si and p-CuO/NiO-TX-100@n-Si exhibits missing of two phases. The SEM images pure p-CuO@n-Si particle structure is cluster but, in contrast the pure p-NiO@n-Si exhibit spherical shape with identical particle. The Triton X-100 blend with CuO/NiO resulting the reduction of particle size in between 20 and 40 nm for p-CuO/NiO-TX-100@n-Si confirmed by TEM analysis. The photo-response characteristics of p-CuO/NiO-TX-100@n-Si Schottky barrier diode exhibit minimum ideality factor (n) (2.64), maximum barrier height (ΦB) (0.78 eV), maximum photosensitivity (Ps) ∼ 6.62 × 102%, photoresponsivity (R) ∼ 598.61 mA/W, external quantum efficiency (EQE)∼ 325.75 %, and specific detectivity (D*) ∼ 11.08 × 1012 (Jones) when compared other diodes.

MatChat: A large language model and application service platform for materials science

The prediction of chemical synthesis pathways plays a pivotal role in materials science research. Challenges, such as the complexity of synthesis pathways and the lack of comprehensive datasets, currently hinder our ability to predict these chemical processes accurately. However, recent advancements in generative artificial intelligence (GAI), including automated text generation and question–answering systems, coupled with fine-tuning techniques, have facilitated the deployment of large-scale AI models tailored to specific domains. In this study, we harness the power of the LLaMA2-7B model and enhance it through a learning process that incorporates 13878 pieces of structured material knowledge data. This specialized AI model, named MatChat, focuses on predicting inorganic material synthesis pathways. MatChat exhibits remarkable proficiency in generating and reasoning with knowledge in materials science. Although MatChat requires further refinement to meet the diverse material design needs, this research undeniably highlights its impressive reasoning capabilities and innovative potential in materials science. MatChat is now accessible online and open for use, with both the model and its application framework available as open source. This study establishes a robust foundation for collaborative innovation in the integration of generative AI in materials science.

Analysis of the Zagreb Indices over the Weakly Zero-Divisor Graph of the Ring Zp×Zt×Zs

Let R be a commutative ring with identity, and Z(R) be the set of zero-divisors of R. The weakly zero-divisor graph of R denoted by WΓ(R) is an undirected (simple) graph with vertex set Z(R)*, and two distinct vertices x and y are adjacent, if and only if there exist r∈ann(x) and s∈ann(y), such that rs=0. Importantly, it is worth noting that WΓ(R) contains the zero-divisor graph Γ(R) as a subgraph. It is known that graph theory applications play crucial roles in different areas one of which is chemical graph theory that deals with the applications of graph theory to solve molecular problems. Analyzing Zagreb indices in chemical graph theory provides numerical descriptors for molecular structures, aiding in property prediction and drug design. These indices find applications in QSAR modeling and chemical informatics, contributing to efficient compound screening and optimization. They are essential tools for advancing pharmaceutical and material science research. This research article focuses on the basic properties of the weakly zero-divisor graph of the ring Zp×Zt×Zs, denoted by WΓ(Zp×Zt×Zs), where p, t, and s are prime numbers that may not necessarily be distinct and greater than 2. Moreover, this study includes the examination of various indices and coindices such as the first and second Zagreb indices and coindices, as well as the first and second multiplicative Zagreb indices and coindices of WΓ(Zp×Zt×Zs).

NOMAD: A distributed web-based platform for managing materials science research data

NOMAD: A distributed web-based platform for managing materials science research data