scholarly journals Data-driven learning of process−property−performance relation in laser-induced aqueous manufacturing and integration of ZnO piezoelectric nanogenerator for self-powered nanosensors

Nano Energy ◽  
2021 ◽  
Vol 83 ◽  
pp. 105820
Author(s):  
Ruoxing Wang ◽  
Siyu Liu ◽  
C. Richard Liu ◽  
Wenzhuo Wu
InfoMat ◽  
2019 ◽  
Vol 1 (1) ◽  
pp. 116-125 ◽  
Author(s):  
Chenxiang Ma ◽  
Shengjie Gao ◽  
Xinqi Gao ◽  
Min Wu ◽  
Ruoxing Wang ◽  
...  

Pharmaceutics ◽  
2019 ◽  
Vol 11 (5) ◽  
pp. 226 ◽  
Author(s):  
Weidong Huang ◽  
Yuan Hou ◽  
Xinyi Lu ◽  
Ziyun Gong ◽  
Yaoyao Yang ◽  
...  

In pharmaceutical nanotechnology, the intentional manipulation of working processes to fabricate nanoproducts with suitable properties for achieving the desired functional performances is highly sought after. The following paper aims to detail how a modified coaxial electrospraying has been developed to create ibuprofen-loaded hydroxypropyl methylcellulose nanoparticles for improving the drug dissolution rate. During the working processes, a key parameter, i.e., the spreading angle of atomization region (θ, °), could provide a linkage among the working process, the property of generated nanoparticles and their functional performance. Compared with the applied voltage (V, kV; D = 2713 − 82V with RθV2 = 0.9623), θ could provide a better correlation with the diameter of resultant nanoparticles (D, nm; D = 1096 − 5θ with RDθ2 = 0.9905), suggesting a usefulness of accurately predicting the nanoparticle diameter. The drug released from the electrosprayed nanoparticles involved both erosion and diffusion mechanisms. A univariate quadratic equation between the time of releasing 95% of the loaded drug (t, min) and D (t = 38.7 + 0.097D − 4.838 × 105D2 with a R2 value of 0.9976) suggests that the nanoparticle diameter has a profound influence on the drug release performance. The clear process-property-performance relationship should be useful for optimizing the electrospraying process, and in turn for achieving the desired medicated nanoparticles.


2020 ◽  
Vol 12 (21) ◽  
pp. 8926
Author(s):  
Qian Cheng ◽  
Xiaobei Jiang ◽  
Haodong Zhang ◽  
Wuhong Wang ◽  
Chunwen Sun

Driver’s driving actions on pedals can be regarded as an expression of driver’s acceleration/deceleration intention. Quickly and accurately detecting driving action intensity on pedals can have great contributions in preventing road traffic accidents and managing the energy consumption. In this paper, we report a pressure-sensitive and self-powered material named triboelectric nano-generators (TENGs). The generated voltage data of TENGs, which is associated with the pedal action, can be collected easily and stored sequentially. According to the characteristics of the voltage data, we have employed a hybrid machine learning method. After collecting signals from TENGs and driving simulator simultaneously, an unsupervised Gaussian mixture model is used to cluster the pedal events automatically using data from simulator. Then, multi-feature candidates of the voltage data from TENGs are extracted and ranked. A supervised random forest model that treats voltage data of TENGs as input data is trained and tested. Results show that data from TENGs can have a high accuracy of more than 90% using the random forest algorithm. The evaluating results demonstrate the accuracy of the proposed data-driven hybrid learning algorithm for recognition of driver’s pedal action intensity. Furthermore, technical and economic characteristics of TENGs and some common sensors are compared and discussed. This work may demonstrate the feasibility of using these data-driven methods on the detection of driver’s pedal action intensity.


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