Design And Application Research Of Vermiculite-based Energy Harvesting Device

Layered materials have become a hot spot in the field of nanomaterials research.Vermiculite,as a kind of natural layered material,is abundant in nature,has the characteristics of heat insulation and heat preservation,ion exchange,etc.,and has shown great potential for applications in the fields of adsorption and catalysis.Two-dimensional nanosheet layers can be obtained by swelling and exfoliating vermiculite masses.In this thesis,vermiculite nanosheets are prepared by liquid-phase exfoliation method,and then micro and nano energy harvesting devices are constructed and used for salt differential energy harvesting as well as frictional electric motion sensors.The details of the research are as follows:1.Self-supportable flexible vermiculite films assembled based on vermiculite nanosheets are constructed for salinity gradient energy harvesting.Two-dimensional vermiculite nanosheet layer solutions were obtained by a two-step ion exchange method followed by their further exfoliation with the help of hydrogen peroxide.The thickness of the individual nanosheet layers was between 1 and 2 nm,ranging in size from a few nanometers to a few microns,and the nanosheets showed negative electrical properties with an overall smooth and flat surface.The vermiculite nanosheets were assembled into vermiculite thin films with uniform layer spacing using vacuum extraction,and the morphological structure and composition of the vermiculite nanosheets and vermiculite thin films were analyzed using XRD,SEM,and AFM characterization.The transport behavior of ions through the vermiculite films was investigated using the nanochannels of the vermiculite films as nanofluidic channels for ion transport.The results exhibit a typical surface charge-controlled ion transport model,and this behavior remains applicable over a wide p H range.It was also successfully used to harvest energy from salinity gradients,with a maximum output power density of 0.26 W/m~2 at a concentration gradient of 1000 times KCl,while when real seawater and tap water were used to observe its power generation capability,the vermiculite film could generate up to 0.13 W/m~2 in seawater and tap water as the loading resistance increased.Also,this thesis investigated the vermiculite film’s thermal stability was investigated and the results showed that its excellent thermal stability enables the vermiculite film to maintain essentially constant ionic conductivity even after heat treatment at 500°C.This provides new ideas for designing nanofluidic membranes from natural minerals,offering opportunities for fabricating nanofluidic devices,and the advantages of this simple,large-scale constructability make nanofluidic transport channels have greater potential for applications in the direction of energy storage and conversion.2.A composite paper based on vermiculite nanosheets and algal fibers was prepared and used as electrodes to design a frictional electrical sensing device for human motion detection and prediction.By loading vermiculite nanosheets,the flame retardant properties,mechanical properties and frictional electrical output properties of the composite paper were significantly improved compared with those of pure algal fiber paper.The negatively charged vermiculite nanoflakes act as a binder to enhance the mechanical strength of the paper by forming hydrogen bonds with the fibers,while the surface roughness of the paper increases with the addition of vermiculite nanoflakes,resulting in an increase in the frictional electrical output.At the same time,the frictional power generation device based on composite paper is not only small and convenient,but also has excellent stability,with no significant change in output performance after more than 10,000 endurance tests and100 days of stability tests,which provides a good foundation for subsequent practical applications.In addition,we used the device for tiny energy harvesting and real-time human motion detection at low weak frequencies.The results show that the device not only successfully collects energy generated by human motion but also responds to signals with significant regularity and variability.We used the machine learning algorithm PCA analysis to reduce the dimensionality of the data,not only the dense arrangement of individual movements of the same type can be easily identified,but also the clusters representing individual movements can be clearly and effectively distinguished from each other;the confusion matrix map of nine different movement types was calculated using KNN classification,showing a prediction accuracy of 96.2%;and the prediction accuracy for different bending angles of the same joint prediction accuracy of up to 99.8%.Our results provide a robust solution for predicting complex human joint motions.It shows that the sensor can be used for various human motion detection and physiological signal monitoring,and has great potential for applications in the fields of rehabilitation training,human-computer interaction,intelligent robotics,and healthcare.