Cryo treatment and corrosion studies of nickel-titanium shape-memory alloy

In nanomaterials and microstructural evolution, electron microscopy has had an important effect on materials investigation. Transmission Electron Microscopy (TEM), Scanning Electron Microscopy (SEM), Energy-Dispersive X-ray Spectroscopy (EDS), Electron Diffraction, Operando Electron Microscopy, and Aberration-Corrected Electron Microscopy offer the investigation on understanding of nanoscale material properties and structure. The present research covers the basics, advantages and disadvantages, and material-related applications of various electron microscopy techniques. TEM is useful for investigating atomic arrangements and imperfections in materials, while SEM offers micro- to nanoscale topographical, morphological, & compositional information. EDS, frequently employed with SEM or TEM, analyzes elements and compounds to determine material compositions. Opando Electron Microscopy allows researchers to observe and assess materials during catalytic reactions and battery charge/discharge cycles. This approach is vital for knowing how dynamic processes influence nanoscale material characteristics and behaviour. Another advanced technique, Aberration-Corrected Electron Microscopy, corrects lens aberrations that interfered with electron microscope resolution. This adjustment enables imaging at sample-limited resolutions, allowing further studies of atomic structures & flaws. These electron microscopy methods significantly improve nanomaterial microstructural evolution understanding. These have enabled extensive study of flaws, transitions between phases, and formation mechanisms, which are essential to producing optimal materials.

Section: F) Operando Electron Microscopymentioning

confidence: 99%

A Comprehensive Review of Electron Microscopy in Materials Science: Technological Advances and Applications

Agrawal,

Prasad,

Nijhawan

et al. 2024

“…Smart materials possess the capability to facilitate the transfer of a specific form of energy. For instance, optical fibres are adept at transmitting electromagnetic waves [8,9]. Activated materials can be classified into two distinct categories.…”

Section: Literature Reviewmentioning

confidence: 99%

Smart Materials for Sensing and Actuation: State-of-the-Art and Prospects

jain,

Sunil,

Hasan

et al. 2024

This research paper provides a comprehensive review of the current state-of-the-art and prospects of smart materials for sensing and actuation applications. Smart materials, with their unique ability to respond to external stimuli, have been the subject of extensive research and development in recent years. The paper begins by discussing the various types of smart materials, including piezoelectric materials, shape memory alloys, and electroactive polymers, and their properties and applications in sensing and actuation. the paper covers the advancements in the design and fabrication of smart materials and devices, including the use of nanotechnology and 3D printing. The potential for integration with emerging technologies, such as artificial intelligence and the internet of things, is also explore. The paper provides a comprehensive and in-depth analysis of the state-of-the-art and prospects of smart materials for sensing and actuation applications. The research presented has significant implications for the development of nextgeneration smart materials and devices, with the potential to revolutionize various industries and improve our quality of life.

“…are only some of the common applications of ML. ML is used to a new area of research because of its vast potential and high efficiency [11]. The employment of high-quality components and gear over a lengthy period of time is essential to the success of conventional research techniques, especially in the biological and some chemical domains.…”

Section: Literature Reviewmentioning

confidence: 99%

Machine Learning and Artificial Intelligence for Advanced Materials Processing: A review on opportunities and challenges

Srivastava,

Kumar,

Kumar

et al. 2024

This research paper explores the opportunities and challenges associated with the use of machine learning and artificial intelligence in advanced materials processing. With the exponential growth of data, advanced analytical techniques and powerful computational tools, machine learning and artificial intelligence can be leveraged to develop novel materials with tailored properties, enhance process optimization, and improve manufacturing efficiencies. However, the integration of these technologies into materials processing systems is not without challenges, including data acquisition and pre-processing, algorithm selection and optimization, and the interpretation of results. This paper provides an overview of the state-of-the-art in machine learning and artificial intelligence for advanced materials processing, highlighting case studies and examples of successful applications, and identifying potential future research directions. The goal of this research is to provide insights and recommendations to accelerate the adoption of these technologies and their impact on the development of advanced materials.