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1. Nanomaterial‐Enabled Flexible and Stretchable Sensing Systems: Processing, Integration, and Applications

   https://doi.org/10.1002/adma.201902343  

   Yao, Shanshan; Ren, Ping; Song, Runqiao; Liu, Yuxuan; Huang, Qijin; Dong, Jingyan; O'Connor, Brendan_T; Zhu, Yong (August 2019, Advanced Materials)

   Abstract Nanomaterial‐enabled flexible and stretchable electronics have seen tremendous progress in recent years, evolving from single sensors to integrated sensing systems. Compared with nanomaterial‐enabled sensors with a single function, integration of multiple sensors is conducive to comprehensive monitoring of personal health and environment, intelligent human–machine interfaces, and realistic imitation of human skin in robotics and prosthetics. Integration of sensors with other functional components promotes real‐world applications of the sensing systems. Here, an overview of the design and integration strategies and manufacturing techniques for such sensing systems is given. Then, representative nanomaterial‐enabled flexible and stretchable sensing systems are presented. Following that, representative applications in personal health, fitness tracking, electronic skins, artificial nervous systems, and human–machine interactions are provided. To conclude, perspectives on the challenges and opportunities in this burgeoning field are considered. 

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2. Zero-crosstalk silicon photonic refractive index sensor with subwavelength gratings

   https://doi.org/10.1186/s40580-024-00446-1  

   Ahmed, Syed_Z; Hasan, Mehedi; Kim, Kyungtae; Kim, Sangsik (September 2024, Nano Convergence)

   Abstract Silicon photonic index sensors have received significant attention for label-free bio and gas-sensing applications, offering cost-effective and scalable solutions. Here, we introduce an ultra-compact silicon photonic refractive index sensor that leverages zero-crosstalk singularity responses enabled by subwavelength gratings. The subwavelength gratings are precisely engineered to achieve an anisotropic perturbation-led zero-crosstalk, resulting in a single transmission dip singularity in the spectrum that is independent of device length. The sensor is optimized for the transverse magnetic mode operation, where the subwavelength gratings are arranged perpendicular to the propagation direction to support a leaky-like mode and maximize the evanescent field interaction with the analyte space. Experimental results demonstrate a high wavelength sensitivity of − 410 nm/RIU and an intensity sensitivity of 395 dB/RIU, with a compact device footprint of approximately 82.8 μm². Distinct from other resonant and interferometric sensors, our approach provides an FSR-free single-dip spectral response on a small device footprint, overcoming common challenges faced by traditional sensors, such as signal/phase ambiguity, sensitivity fading, limited detection range, and the necessity for large device footprints. This makes our sensor ideal for simplified intensity interrogation. The proposed sensor holds promise for a range of on-chip refractive index sensing applications, from gas to biochemical detection, representing a significant step towards efficient and miniaturized photonic sensing solutions. Graphical Abstract 

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3. Stretchable Sensors for Soft Robotic Grippers in Edge-Intelligent IoT Applications

   https://doi.org/10.3390/s23084039  

   Ghosh, Prosenjit Kumar; Sundaravadivel, Prabha (April 2023, Sensors)

   The rapid development of electronic material and sensing technology has enabled research to be conducted on liquid metal-based soft sensors. The application of soft sensors is widespread and has many applications in soft robotics, smart prosthetics, and human-machine interfaces, where these sensors can be integrated for precise and sensitive monitoring. Soft sensors can be easily integrated for soft robotic applications, where traditional sensors are incompatible with robotic applications as these types of sensors show large deformation and very flexible. These liquid-metal-based sensors have been widely used for biomedical, agricultural and underwater applications. In this research, we have designed and fabricated a novel soft sensor that yields microfluidic channel arrays embedded with liquid metal Galinstan alloy. First of all, the article presents different fabrication steps such as 3D modeling, printing, and liquid metal injection. Different sensing performances such as stretchability, linearity, and durability results are measured and characterized. The fabricated soft sensor demonstrated excellent stability and reliability and exhibited promising sensitivity with respect to different pressures and conditions. 

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4. Recent advances and applications of deep learning methods in materials science

   https://doi.org/10.1038/s41524-022-00734-6  

   Choudhary, Kamal; DeCost, Brian; Chen, Chi; Jain, Anubhav; Tavazza, Francesca; Cohn, Ryan; Park, Cheol Woo; Choudhary, Alok; Agrawal, Ankit; Billinge, Simon J.; et al (December 2022, npj Computational Materials)

   Abstract Deep learning (DL) is one of the fastest-growing topics in materials data science, with rapidly emerging applications spanning atomistic, image-based, spectral, and textual data modalities. DL allows analysis of unstructured data and automated identification of features. The recent development of large materials databases has fueled the application of DL methods in atomistic prediction in particular. In contrast, advances in image and spectral data have largely leveraged synthetic data enabled by high-quality forward models as well as by generative unsupervised DL methods. In this article, we present a high-level overview of deep learning methods followed by a detailed discussion of recent developments of deep learning in atomistic simulation, materials imaging, spectral analysis, and natural language processing. For each modality we discuss applications involving both theoretical and experimental data, typical modeling approaches with their strengths and limitations, and relevant publicly available software and datasets. We conclude the review with a discussion of recent cross-cutting work related to uncertainty quantification in this field and a brief perspective on limitations, challenges, and potential growth areas for DL methods in materials science. 

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5. Artificial intelligence and machine learning in glass science and technology: 21 challenges for the 21st century

   Ravinder, R.; Venugopal, Vineeth; Bishnoi, Suresh; Singh, Sourabh; Zaki, Mohd; Grover, Hargun Singh; Bauchy, Mathieu; Agarwal, Manish; Krishnan, N. M. (July 2021, International journal of applied glass science)

   null (Ed.)

   Glasses have been an integral part of human life for more than 2000 years. Despite several years of research and analysis, some fundamental and practical questions on glasses still remain unanswered. While most of the earlier approaches were based on (i) expert knowledge and intuition, (ii) Edisonian trial and error, or (iii) physics-driven modeling and analysis, recent studies suggest that data-driven techniques, such as artificial intelligence (AI) and machine learning (ML), can provide fresh perspectives to tackle some of these questions. In this article, we identify 21 grand challenges in glass science, the solutions of which are either enabling AI and ML or enabled by AI and ML to accelerate the field of glass science. The challenges presented here range from fundamental questions related to glass formation and composition–processing–property relationships to industrial problems such as automated flaw detection in glass manufacturing. We believe that the present article will instill enthusiasm among the readers to explore some of the grand challenges outlined here and to discover many more challenges that can advance the field of glass science, engineering, and technology. 

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