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A machine learning-guided design and manufacturing of wearable nanofibrous acoustic energy harvesters
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作者 negar hosseinzadeh kouchehbaghi Maryam Yousefzadeh +8 位作者 Aliakbar Gharehaghaji Safoora Khosravi Danial Khorsandi Reihaneh Haghniaz Ke Cao Mehmet R.Dokmeci Mohammad Rostami Ali Khademhosseini Yangzhi Zhu 《Nano Research》 SCIE EI CSCD 2024年第10期9181-9192,共12页
Nanofibrous acoustic energy harvesters(NAEHs)have emerged as promising wearable platforms for efficient noise-to-electricity conversion in distributed power energy systems and wearable sound amplifiers for assistive l... Nanofibrous acoustic energy harvesters(NAEHs)have emerged as promising wearable platforms for efficient noise-to-electricity conversion in distributed power energy systems and wearable sound amplifiers for assistive listening devices.However,their reallife efficacy is hampered by low power output,particularly in the low-frequency range(<1 kHz).This study introduces a novel approach to enhance the performance of NAEHs by applying machine learning(ML)techniques to guide the synthesis of electrospun polyvinylidene fluoride(PVDF)/polyurethane(PU)nanofibers,optimizing their application in wearable NAEHs.We use a feed-forward neural network along with solving an optimization problem to find the optimal input values of the electrospinning(applied voltage,nozzle-collector distance,electrospinning time,and drum rotation speed)to generate maximum output performance(acoustic-to-electricity conversion efficiency).We first prepared a dataset to train the network to predict the output power given the input variables with high accuracy.Upon introducing the neural network,we fix the network and then solve an optimization problem using a genetic algorithm to search for the input values that lead to the maximum energy harvesting efficiency.Our ML-guided wearable PVDF/PU NAEH platform can deliver a maximal acoustoelectric power density output of 829μW/cm^(3) within the surrounding noise levels.In addition,our system can function stably in a broad frequency(0.1-2 kHz)with a high energy conversion efficiency of 66%.Sound recognition analysis reveals a robust correlation exceeding 0.85 among lexically akin terms with varying sound intensities,contrasting with a diminished correlation below 0.27 for words with disparate semantic connotations.Overall,this work provides a previously unexplored route to utilize ML in advancing wearable NAEHs with excellent practicability. 展开更多
关键词 wearable electronics acoustic energy harvester machine learning piezoelectric nanogenerator electrospun nanofiber
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Biomedical applications of engineered heparin-based materials
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作者 Ehsan Nazarzadeh Zare Danial Khorsandi +18 位作者 Atefeh Zarepour Hulya Yilmaz Tarun Agarwal Sara Hooshmand Reza Mohammadinejad Fatma Ozdemir Onur Sahin Sevin Adiguzel Haroon Khan Ali Zarrabi Esmaeel Sharifi Arun Kumar Ebrahim Mostafavi negar hosseinzadeh kouchehbaghi Virgilio Mattoli Feng Zhang Vadim Jucaud Alireza Hassani Najafabadi Ali Khademhosseini 《Bioactive Materials》 SCIE 2024年第1期87-118,共32页
Heparin is a negatively charged polysaccharide with various chain lengths and a hydrophilic backbone.Due to its fascinating chemical and physical properties,nontoxicity,biocompatibility,and biodegradability,heparin ha... Heparin is a negatively charged polysaccharide with various chain lengths and a hydrophilic backbone.Due to its fascinating chemical and physical properties,nontoxicity,biocompatibility,and biodegradability,heparin has been extensively used in different fields of medicine,such as cardiovascular and hematology.This review highlights recent and future advancements in designing materials based on heparin for various biomedical applications.The physicochemical and mechanical properties,biocompatibility,toxicity,and biodegradability of heparin are discussed.In addition,the applications of heparin-based materials in various biomedical fields,such as drug/gene delivery,tissue engineering,cancer therapy,and biosensors,are reviewed.Finally,challenges,opportunities,and future perspectives in preparing heparin-based materials are summarized. 展开更多
关键词 Heparin Nanomaterials Physicochemical Biological Preparation Biomedical applications
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