scholarly journals Review—Organic-Inorganic Hybrid Functional Materials: An Integrated Platform for Applied Technologies

2018 ◽  
Vol 165 (8) ◽  
pp. B3137-B3156 ◽  
Author(s):  
Sajjad Husain Mir ◽  
Larry Akio Nagahara ◽  
Thomas Thundat ◽  
Parvaneh Mokarian-Tabari ◽  
Hidemitsu Furukawa ◽  
...  
Keyword(s):  
Functional Materials ◽  
Hybrid Functional ◽  
Inorganic Hybrid ◽  
Integrated Platform

“Ship-in-a-bottle”, a new synthesis strategy for preparing novel hybrid host–guest nanocomposites for highly selective membrane gas separation

2018 ◽  
Vol 6 (4) ◽  
pp. 1751-1771 ◽  
Author(s):  
Abtin Ebadi Amooghin ◽  
Hamidreza Sanaeepur ◽  
Mohammadreza Omidkhah ◽  
Ali Kargari
Keyword(s):  
Gas Separation ◽  
Functional Materials ◽  
Hybrid Nanocomposites ◽  
Membrane Gas Separation ◽  
Polymeric Matrices ◽  
Inorganic Hybrid ◽  
New Synthesis ◽  
Synthesis Strategy ◽  
Selective Membrane

Organic–inorganic hybrid nanocomposites within polymeric matrices have potential as functional materials for membrane gas separation.

scholarly journals Scanning Electron Microscopy and Atomic Force Microscopy: Topographic and Dynamical Surface Studies of Blends, Composites, and Hybrid Functional Materials for Sustainable Future

2019 ◽  
Vol 2019 ◽  
pp. 1-16 ◽  
Author(s):  
Joanna Rydz ◽  
Alena Šišková ◽  
Anita Andicsová Eckstein
Keyword(s):  
Functional Materials ◽  
Degradation Process ◽  
Hybrid Functional ◽  
Surface Studies ◽  
Aliphatic Polyesters ◽  
Force Microscopy ◽  
Atomic Force ◽  
Materials Used ◽  
Physical And Chemical ◽  
Microscopic Techniques

Microscopic techniques are often used in material science, enabling the assessment of the morphology, composition, physical properties, and dynamic behaviour of materials. The review focuses on the topographic and dynamical surface studies of (bio)degradable polymers, in particular aliphatic polyesters, the most promising ones. The (bio)degradation process promotes physical and chemical changes in material properties that can be characterised by microscopic techniques. These changes occurring both under controlled conditions as well as in the processing stage or during use indicate morphological and structural transformations resulting from the deterioration of the material and have a significant impact on the characteristic of materials used in many applications, for example, for use as packaging.

scholarly journals Gas Sensors Based on Chemi-Resistive Hybrid Functional Nanomaterials

2020 ◽  
Vol 12 (1) ◽  
Author(s):  
Yingying Jian ◽  
Wenwen Hu ◽  
Zhenhuan Zhao ◽  
Pengfei Cheng ◽  
Hossam Haick ◽  
...  
Keyword(s):  
Gas Sensors ◽  
Carrier Transport ◽  
Gas Sensing ◽  
Low Cost ◽  
Functional Materials ◽  
Fast Response ◽  
Hybrid Functional ◽  
Great Stability ◽  
Molecular Binding ◽  
Response Recovery

AbstractChemi-resistive sensors based on hybrid functional materials are promising candidates for gas sensing with high responsivity, good selectivity, fast response/recovery, great stability/repeatability, room-working temperature, low cost, and easy-to-fabricate, for versatile applications. This progress report reviews the advantages and advances of these sensing structures compared with the single constituent, according to five main sensing forms: manipulating/constructing heterojunctions, catalytic reaction, charge transfer, charge carrier transport, molecular binding/sieving, and their combinations. Promises and challenges of the advances of each form are presented and discussed. Critical thinking and ideas regarding the orientation of the development of hybrid material-based gas sensor in the future are discussed.

scholarly journals A Hybrid Microstructure Piezoresistive Sensor with Machine Learning Approach for Gesture Recognition

2021 ◽  
Vol 11 (16) ◽  
pp. 7264
Author(s):  
Yousef Al-Handarish ◽  
Olatunji Mumini Omisore ◽  
Jing Chen ◽  
Xiuqi Cao ◽  
Toluwanimi Oluwadara Akinyemi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Flexible Electronics ◽  
Short Term Memory ◽  
Functional Materials ◽  
Tactile Feedback ◽  
Classification Model ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Hybrid Functional ◽  
Sensor Structure

Developments in flexible electronics have adopted various approaches which have enhanced the applicability of human–machine interface fields. Recently, microstructural integration and hybrid functional materials were designed for realizing human somatosensory. Nonetheless, designing tactile sensors with smart structures using facile and low-cost fabrication processes remains challenging. Furthermore, using the sensors for recognizing stimuli and feedback applications remains poorly validated. In this study, a highly flexible piezoresistive tactile sensor was developed by homogeneously dispersing carbon black (CB) in a microstructure porous sugar/PDMS-based sponge. Owning to its high flexibility and softness, the sensor can be mounted on human or robotic systems for different clinical applications. We validated the applicability of the proposed sensor by applying it to recognizing grasp and release forces in an open setting and to classifying hand motions that surgeons apply on the master interface of a robotic system during intravascular catheterization. For this purpose, we implemented the long short-term memory (LSTM)-dense classification model and five traditional machine learning methods, namely, support vector machine, multilayer perceptron, decision tree, and k-nearest neighbor. The models were used to classify the different hand gestures obtained in an open-setting experiment. Amongst all, the LSTM-dense method yielded the highest overall recognition accuracy (87.38%). Nevertheless, the performance of the other models was in a similar range, showing that our sensor structure can be applied in intelligence sensing or tactile feedback systems. Secondly, the sensor prototype was applied to analyze the motions made while manipulating an interventional robot. We analyzed the displacement and velocity of the master interface during typical axial (push/pull) and radial operations with the robot. The results obtained show that the sensor is capable of recording unique patterns during different operations. Thus, a combination of the flexible wearable sensors and machine learning could yield a future generation of flexible materials and artificial intelligence of things (AIoT) devices.

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