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Jun Chen

Researcher at Shanghai Jiao Tong University

Publications -  2300
Citations -  100809

Jun Chen is an academic researcher from Shanghai Jiao Tong University. The author has contributed to research in topics: Medicine & Chemistry. The author has an hindex of 136, co-authored 1856 publications receiving 77368 citations. Previous affiliations of Jun Chen include Peking Union Medical College & Nankai University.

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Sensing-transducing coupled piezoelectric textiles for self-powered humidity detection and wearable biomonitoring.

TL;DR: In this paper , a sensing-transducing coupled strategy was proposed by embedding the high piezoresponse Sm-PMN-PT ceramic (d33 = ∼1500 pC N-1) into a moisture sensitive polyetherimide (PEI) polymer matrix via electrospinning to conjugate the humidity perception and signal transduction synchronously and sympatrically.
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Controllable assembly of CeO2 micro/nanospheres with adjustable size and their application in Cr(VI) adsorption

TL;DR: CeO2 micro/nanospheres with perfect spherical morphology, large specific surface area, high porosity, high stability and adjustable diameter were synthesized from a simple system, and the stirring-time before hydrothermal process was found to be the key factor for the final particle size control.
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Size of the Largest Lymph Node Visualized on Multi-Detector-Row Computed Tomography (MDCT) is Useful in Predicting Metastatic Lymph Node Status of Gastric Cancer

TL;DR: The size of the largest lymph node in terms of LAD and SAD visualized on MDCT was useful for predicting the MLN status of gastric cancer, with accuracy comparable to the traditional MDCT method of counting the total number of MLNs detected.
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Performance enhancement of single-walled nanotube–microwave exfoliated graphene oxide composite electrodes using a stacked electrode configuration

TL;DR: In this paper, a stacked electrode supercapacitor cell using stainless steel meshes as the current collectors and optimised single walled nanotubes (SWNT)-microwave exfoliated graphene oxide (mw rGO) composites as the electrode material.
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Die wear prediction by defining three-stage coefficient K for AHSS sheet metal forming process

TL;DR: In this article, the authors defined wear coefficient K for three stages of wearing process, and approximated by pin-on-disc wear experimental results of JIS SKD11 tooling material.