2022
DOI: 10.1007/s40820-022-00874-w
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Morphological Engineering of Sensing Materials for Flexible Pressure Sensors and Artificial Intelligence Applications

Abstract: Highlights Various morphological structures in pressure sensors with the resulting advanced sensing properties are reviewed comprehensively. Relevant manufacturing techniques and intelligent applications of pressure sensors are summarized in a complete and interesting way. Future challenges and perspectives of flexible pressure sensors are critically discussed.

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Cited by 122 publications
(84 citation statements)
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References 280 publications
(839 reference statements)
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“…25 Therefore, machine learning (ML) is one of the most rapidly developing and significant subfields of AI research. 26–33 At the very beginning of ML development (1950s–1960s), there are three major branches, that is, symbolic learning proposed by Hunt et al , statistical methods by Nilsson, and neural networks by Rosenblatt. 34 Nowadays, these branches develop advanced methods and can be divided into four categories, that is, classification, regression, clustering, and dimensionality reduction.…”
Section: Introductionmentioning
confidence: 99%
“…25 Therefore, machine learning (ML) is one of the most rapidly developing and significant subfields of AI research. 26–33 At the very beginning of ML development (1950s–1960s), there are three major branches, that is, symbolic learning proposed by Hunt et al , statistical methods by Nilsson, and neural networks by Rosenblatt. 34 Nowadays, these branches develop advanced methods and can be divided into four categories, that is, classification, regression, clustering, and dimensionality reduction.…”
Section: Introductionmentioning
confidence: 99%
“…Such structural or compositional variations and their cooperations could accelerate the interaction of sensing materials with analytes or stimuli and amplify the conversion of generated signals, from both chemistry and physics, finally offering the possibility for the optimization and improvement of sensor performance. [1,7,19,22,33,34] Accordingly, a wide range of sensors based on laser-texturing micro/nanostructures have been achieved and explored extensively, which is being used in wearable devices in biomedicine, energy, and environment field. The continuous innovation of laser micro/ nanofabrication will fuel the progress of making sensors smarter.…”
Section: Introductionmentioning
confidence: 99%
“…These sensors can convert physical, chemical, biological, and other signals collected into easily identifiable signals like electrical or optical ones, thus being employed for the smart control of household appliances, environmental monitoring, and human activity tracking. [ 1,2 ] More remarkably, the prevalent COVID‐19 epidemic imperatively needs high‐performance smart biosensors, accelerating the fast‐moving development of sensors with fast, real‐time, and remote monitoring ability. [ 3 ] As demonstrated, the device functions can be enhanced by regulating its surface chemical composition, [ 4 ] structure, [ 5 ] and materials lattice structure across varied spatial scales.…”
Section: Introductionmentioning
confidence: 99%
“…It has gained considerable attention from various traditional industries such as automotive [ 1 , 2 ], aviation [ 3 , 4 , 5 ], and manufacturing industries [ 6 , 7 , 8 , 9 ] since it can help in controlling the system stability and process flow. Recently, owing to the advances in materials science, mechanical engineering, and fabrication technologies, the pressure sensor can be implemented with various materials and dimension, enabling its application in a wide range of emerging academia disciplines and industrial fields, such as robotics [ 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 ], artificial intelligence [ 18 , 19 , 20 , 21 ], and smart factory [ 22 , 23 ]. Among them, the use of pressure sensors in the biomedical engineering field is one of the representative promising applications of this technology because it can help to measure the pressure of the target tissue and organ that is essential for wide range of disease diagnosis and therapy ( Figure 1 a,b) [ 24 , 25 , 26 , 27 , 28 , 29 ].…”
Section: Introductionmentioning
confidence: 99%