The controlled assembly of nanomaterials has demon-strated significant potential in advancing technological devices.However,achieving highly efficient and low-loss assembly technique for nanomate-rials,enabling the cr...The controlled assembly of nanomaterials has demon-strated significant potential in advancing technological devices.However,achieving highly efficient and low-loss assembly technique for nanomate-rials,enabling the creation of hierarchical structures with distinctive func-tionalities,remains a formidable challenge.Here,we present a method for nanomaterial assembly enhanced by ionic liquids,which enables the fabrication of highly stable,flexible,and transparent electrodes featuring an organized layered structure.The utilization of hydrophobic and non-volatile ionic liquids facilitates the production of stable interfaces with water,effectively preventing the sedimentation of 1D/2D nanomaterials assembled at the interface.Furthermore,the interfacially assembled nanomaterial monolayer exhibits an alternate self-climbing behavior,enabling layer-by-layer transfer and the formation of a well-ordered MXene-wrapped silver nanowire network film.The resulting composite film not only demonstrates exceptional photoelectric performance with a sheet resistance of 9.4Ωsq^(-1) and 93%transmittance,but also showcases remarkable environmental stability and mechanical flexibility.Particularly noteworthy is its application in transparent electromagnetic interference shielding materials and triboelectric nanogenerator devices.This research introduces an innovative approach to manufacture and tailor functional devices based on ordered nanomaterials.展开更多
Background:Diabetic nephropathy(DN)is the most common complication of type 2 diabetes mellitus and the main cause of end-stage renal disease worldwide.Diagnostic biomarkers may allow early diagnosis and treatment of D...Background:Diabetic nephropathy(DN)is the most common complication of type 2 diabetes mellitus and the main cause of end-stage renal disease worldwide.Diagnostic biomarkers may allow early diagnosis and treatment of DN to reduce the prevalence and delay the development of DN.Kidney biopsy is the gold standard for diagnosing DN;however,its invasive character is its primary limitation.The machine learning approach provides a non-invasive and specific criterion for diagnosing DN,although traditional machine learning algorithms need to be improved to enhance diagnostic performance.Methods:We applied high-throughput RNA sequencing to obtain the genes related to DN tubular tissues and normal tubular tissues of mice.Then machine learning algorithms,random forest,LASSO logistic regression,and principal component analysis were used to identify key genes(CES1G,CYP4A14,NDUFA4,ABCC4,ACE).Then,the genetic algorithm-optimized backpropagation neural network(GA-BPNN)was used to improve the DN diagnostic model.Results:The AUC value of the GA-BPNN model in the training dataset was 0.83,and the AUC value of the model in the validation dataset was 0.81,while the AUC values of the SVM model in the training dataset and external validation dataset were 0.756 and 0.650,respectively.Thus,this GA-BPNN gave better values than the traditional SVM model.This diagnosis model may aim for personalized diagnosis and treatment of patients with DN.Immunohistochemical staining further confirmed that the tissue and cell expression of NADH dehydrogenase(ubiquinone)1 alpha subcomplex,4-like 2(NDUFA4L2)in tubular tissue in DN mice were decreased.Conclusion:The GA-BPNN model has better accuracy than the traditional SVM model and may provide an effective tool for diagnosing DN.展开更多
叶片覆冰会严重影响风机的安全稳定运行。目前,电热防冰是最高效可靠的风机叶片防冰方法,但存在防冰区域受热不均匀、局部覆冰以及过多分区导致防冰系统过于复杂等问题。为此提出采用正温度系数(positive temperature coefficient,PTC)...叶片覆冰会严重影响风机的安全稳定运行。目前,电热防冰是最高效可靠的风机叶片防冰方法,但存在防冰区域受热不均匀、局部覆冰以及过多分区导致防冰系统过于复杂等问题。为此提出采用正温度系数(positive temperature coefficient,PTC)材料进行风机叶片自适应电加热防冰的创新方法,通过原位聚合法成功制备了一种低居里点PTC材料,其居里温度点为1℃。随后,基于该材料的阻-温特性,建立了风机叶片的电加热防冰模型,并进行数值模拟。研究结果显示,当采用低居里点PTC材料进行风机叶片电加热防冰时,无需进行防冰区域的分区,就能使得防冰区域受热更加均匀。在一定的工作电压下,低居里点PTC材料在不同环境温度和风速下展现出自适应调节加热功率的能力,并且经过100次循环阻-温测试后,材料仍具有极强的自适应调节能力。最后,通过试验验证了材料的这种自适应调节能力。该研究结果为后续基于低居里点PTC材料的风机叶片防冰系统的研究奠定了坚实基础。展开更多
基金This work was supported by the National Natural Science Foundation of China(nos.21988102,and 22305026)the China Postdoctoral Science Foundation(2019M650433).
文摘The controlled assembly of nanomaterials has demon-strated significant potential in advancing technological devices.However,achieving highly efficient and low-loss assembly technique for nanomate-rials,enabling the creation of hierarchical structures with distinctive func-tionalities,remains a formidable challenge.Here,we present a method for nanomaterial assembly enhanced by ionic liquids,which enables the fabrication of highly stable,flexible,and transparent electrodes featuring an organized layered structure.The utilization of hydrophobic and non-volatile ionic liquids facilitates the production of stable interfaces with water,effectively preventing the sedimentation of 1D/2D nanomaterials assembled at the interface.Furthermore,the interfacially assembled nanomaterial monolayer exhibits an alternate self-climbing behavior,enabling layer-by-layer transfer and the formation of a well-ordered MXene-wrapped silver nanowire network film.The resulting composite film not only demonstrates exceptional photoelectric performance with a sheet resistance of 9.4Ωsq^(-1) and 93%transmittance,but also showcases remarkable environmental stability and mechanical flexibility.Particularly noteworthy is its application in transparent electromagnetic interference shielding materials and triboelectric nanogenerator devices.This research introduces an innovative approach to manufacture and tailor functional devices based on ordered nanomaterials.
基金the National Natural Science Foundation of China(Grant Number:81970631 to W.L.).
文摘Background:Diabetic nephropathy(DN)is the most common complication of type 2 diabetes mellitus and the main cause of end-stage renal disease worldwide.Diagnostic biomarkers may allow early diagnosis and treatment of DN to reduce the prevalence and delay the development of DN.Kidney biopsy is the gold standard for diagnosing DN;however,its invasive character is its primary limitation.The machine learning approach provides a non-invasive and specific criterion for diagnosing DN,although traditional machine learning algorithms need to be improved to enhance diagnostic performance.Methods:We applied high-throughput RNA sequencing to obtain the genes related to DN tubular tissues and normal tubular tissues of mice.Then machine learning algorithms,random forest,LASSO logistic regression,and principal component analysis were used to identify key genes(CES1G,CYP4A14,NDUFA4,ABCC4,ACE).Then,the genetic algorithm-optimized backpropagation neural network(GA-BPNN)was used to improve the DN diagnostic model.Results:The AUC value of the GA-BPNN model in the training dataset was 0.83,and the AUC value of the model in the validation dataset was 0.81,while the AUC values of the SVM model in the training dataset and external validation dataset were 0.756 and 0.650,respectively.Thus,this GA-BPNN gave better values than the traditional SVM model.This diagnosis model may aim for personalized diagnosis and treatment of patients with DN.Immunohistochemical staining further confirmed that the tissue and cell expression of NADH dehydrogenase(ubiquinone)1 alpha subcomplex,4-like 2(NDUFA4L2)in tubular tissue in DN mice were decreased.Conclusion:The GA-BPNN model has better accuracy than the traditional SVM model and may provide an effective tool for diagnosing DN.
文摘叶片覆冰会严重影响风机的安全稳定运行。目前,电热防冰是最高效可靠的风机叶片防冰方法,但存在防冰区域受热不均匀、局部覆冰以及过多分区导致防冰系统过于复杂等问题。为此提出采用正温度系数(positive temperature coefficient,PTC)材料进行风机叶片自适应电加热防冰的创新方法,通过原位聚合法成功制备了一种低居里点PTC材料,其居里温度点为1℃。随后,基于该材料的阻-温特性,建立了风机叶片的电加热防冰模型,并进行数值模拟。研究结果显示,当采用低居里点PTC材料进行风机叶片电加热防冰时,无需进行防冰区域的分区,就能使得防冰区域受热更加均匀。在一定的工作电压下,低居里点PTC材料在不同环境温度和风速下展现出自适应调节加热功率的能力,并且经过100次循环阻-温测试后,材料仍具有极强的自适应调节能力。最后,通过试验验证了材料的这种自适应调节能力。该研究结果为后续基于低居里点PTC材料的风机叶片防冰系统的研究奠定了坚实基础。
