电控离子交换技术(electrochemically switched ion exchange,ESIX)是将电活性离子交换材料(EXIMs)沉积或涂覆在导电基底上,通过电化学控制导电基底上活性材料氧化还原状态实现目标离子置入与释放,从而实现离子的分离。该技术具有痕量...电控离子交换技术(electrochemically switched ion exchange,ESIX)是将电活性离子交换材料(EXIMs)沉积或涂覆在导电基底上,通过电化学控制导电基底上活性材料氧化还原状态实现目标离子置入与释放,从而实现离子的分离。该技术具有痕量提取、无二次污染、速率可控、高选择性等优点。通过共沉淀法制备Ni Fe Mn LDH,并将其与碳纳米管(CNTs)、聚偏二氟乙烯(PVDF)混合涂覆到石墨板上,制得NiFeMn LDH/CNTs/PVDF膜电极。NiFeMn LDH层板上具有丰富的羟基官能团,可与W(Ⅵ)发生羟基配位;层间的阴离子与W(Ⅵ)进行离子交换,可为W(Ⅵ)提供丰富的活性位点。在ESIX系统中,膜电极对W(Ⅵ)的吸附容量可达122.10 mg·g^(-1),且W(Ⅵ)与Mo(Ⅵ)、Cl^(-)、■分离因子(■)分别为1.25、19.60、35.80,实现了W(Ⅵ)选择性分离。此外,该膜电极具有优异的循环稳定性,为钨的高效分离提供了新的方向。展开更多
Respiration is a critical physiological process of the body and plays an essential role in maintaining human health.Wearable piezoelectric nanofiber-based respiratory monitoring has attracted much attention due to its...Respiration is a critical physiological process of the body and plays an essential role in maintaining human health.Wearable piezoelectric nanofiber-based respiratory monitoring has attracted much attention due to its self-power,high linearity,noninvasiveness,and convenience.However,the limited sensitivity of conventional piezoelectric nanofibers makes it difficult to meet medical and daily respiratory monitoring requirements due to their low electromechanical conversion efficiency.Here,we present a universally applicable,highly sensitive piezoelectric nanofiber characterized by a coaxial composite structure of polyvinylidene fluoride(PVDF)and carbon nanotube(CNT),which is denoted as PS-CC.Based on elucidating the enhancement mechanism from the percolation effect,PS-CC exhibits excellent sensing performance with a high sensitivity of 3.7 V/N and a fast response time of 20 ms for electromechanical conversion.As a proof-of-concept,the nanofiber membrane is seamlessly integrated into a facial mask,facilitating accurate recognition of respiratory states.With the assistance of a one-dimensional convolutional neural network(CNN),a PS-CC-based smart mask can recognize respiratory tracts and multiple breathing patterns with a classification accuracy of up to 97.8%.Notably,this work provides an effective strategy for monitoring respiratory diseases and offers widespread utility for daily health monitoring and clinical applications.展开更多
文摘电控离子交换技术(electrochemically switched ion exchange,ESIX)是将电活性离子交换材料(EXIMs)沉积或涂覆在导电基底上,通过电化学控制导电基底上活性材料氧化还原状态实现目标离子置入与释放,从而实现离子的分离。该技术具有痕量提取、无二次污染、速率可控、高选择性等优点。通过共沉淀法制备Ni Fe Mn LDH,并将其与碳纳米管(CNTs)、聚偏二氟乙烯(PVDF)混合涂覆到石墨板上,制得NiFeMn LDH/CNTs/PVDF膜电极。NiFeMn LDH层板上具有丰富的羟基官能团,可与W(Ⅵ)发生羟基配位;层间的阴离子与W(Ⅵ)进行离子交换,可为W(Ⅵ)提供丰富的活性位点。在ESIX系统中,膜电极对W(Ⅵ)的吸附容量可达122.10 mg·g^(-1),且W(Ⅵ)与Mo(Ⅵ)、Cl^(-)、■分离因子(■)分别为1.25、19.60、35.80,实现了W(Ⅵ)选择性分离。此外,该膜电极具有优异的循环稳定性,为钨的高效分离提供了新的方向。
基金supported by the Sichuan Science and Technology Program(No.2023NSFSC0313)the Basic Research Cultivation Project of Southwest Jiaotong University(No.2682023KJ024).
文摘Respiration is a critical physiological process of the body and plays an essential role in maintaining human health.Wearable piezoelectric nanofiber-based respiratory monitoring has attracted much attention due to its self-power,high linearity,noninvasiveness,and convenience.However,the limited sensitivity of conventional piezoelectric nanofibers makes it difficult to meet medical and daily respiratory monitoring requirements due to their low electromechanical conversion efficiency.Here,we present a universally applicable,highly sensitive piezoelectric nanofiber characterized by a coaxial composite structure of polyvinylidene fluoride(PVDF)and carbon nanotube(CNT),which is denoted as PS-CC.Based on elucidating the enhancement mechanism from the percolation effect,PS-CC exhibits excellent sensing performance with a high sensitivity of 3.7 V/N and a fast response time of 20 ms for electromechanical conversion.As a proof-of-concept,the nanofiber membrane is seamlessly integrated into a facial mask,facilitating accurate recognition of respiratory states.With the assistance of a one-dimensional convolutional neural network(CNN),a PS-CC-based smart mask can recognize respiratory tracts and multiple breathing patterns with a classification accuracy of up to 97.8%.Notably,this work provides an effective strategy for monitoring respiratory diseases and offers widespread utility for daily health monitoring and clinical applications.
