Fig 1 - uploaded by Krishna Murti Chaturvedi
Content may be subject to copyright.
Source publication
Thermoelectric technology offers a reliable, environmentally clean, and
maintenance- free method for power generation and temperature control applications. The drawback of thermoelectric materials has been its low efficiency in comparison with conventional heat engines. The advancement in the last two decades in the understanding of nanoscale effec...
Similar publications
Element doping and nano-inclusion embedding are effective approaches to enhance the electrical conductivities and decrease the lattice thermal conductivities of thermoelectric (TE) materials, respectively. However, the intrinsic low electrical thermal conductivities and high electrical properties are severely sacrificed, and the final figure of mer...
We propose a simple, low-cost, and large-area method to increase the thermoelectric figure of merit (ZT) in silicon membranes by the deposition of an ultrathin aluminum layer. Transmission electron microscopy showed that short deposition of aluminum on a silicon substrate covers the surface with an ultrathin amorphous film, which, according to rece...
Citations
... 1. (a) Seebeck and (b) Peltier effect representation[4]. ...
... [4][5][6][7][8][9][10][11][12][13][14] show that the dielectric storage decreases with increasing frequency. This may be related to the tendency of dipoles in the sample pellets to orient themselves in line with the applied electric field direction[77]. ...
... 4. Comparison of compositional analysis from the EDS data for the pristine and sintered p-type copper selenide samples. ...
... (a) (b) Illustration of Peltier effect (a) and Seeback effect (b) [7] Silicon thermoelectric microcooler Based on the Peltier effect, we designed a cooling system with microcoolers for processors. Figure 2 shows our thermoelectric microcooler for on-chip hot spot cooling. ...
... Illustration of Peltier effect (a) and Seeback effect (b) [7]. for processors. ...
... Illustration of Peltier effect (a) and Seeback effect(b) [7] ...
Chip cooling technologies have been attracting researchers’ interests as the wiring density in integrated circuits increases dramatically in recent chip design. A Peltier effect based thermoelectric cooling system is designed for microprocessor cooling. A 3D Finite Element Analysis model is developed to validate, analyse, and optimize the performance of the thermoelectric cooling system. The effects of microcooler size and activation current have been studied by thermo-electric coupled field analysis. It has been found that the Peltier effect based thermoelectric cooling system can successfully reduce the temperature of microprocessors and the optimum activation current for the cooling system has been determined.
This article highlights the potential of predicting cell parameters, such as potential and current, for the optimization of electrochemical processes. We used Python to make complex ML predictions. Lithium-ion (Li-ion) batteries are rechargeable batteries used for a variety of electronic devices, which range from electric vehicles, smartphones, and even satellites. A set of Li-ion batteries run through different operational profiles (charge, discharge and impedance) at room temperatures, and time. Here, input (features) parameters like, Impedance measurement, Voltage measured, Current measured, Temperature measured, Current load, Voltage load, Time, under charge and discharge cycles, for batteries evaluations and predictions. This NASA dataset is classified as a time-series dataset.
Electrochemistry in battery making is one of the prevalent and proven sciences for Efficient Batteries for the Automobile Industry. With the advent of Material Science and Technology, it also embraced the electrochemistry utility for several electrochemical parameter customizations. Not only that, electrochemistry has been a proven asset for sensor technology and budding defense technology. Meanwhile, the development of nano-/ultrafine-grain metallic materials with improved strength has improved ion trapping and high electrode-potential composites and are prominent potential candidates for future fuel cell and Hydrogen trapping based cell’s technologies.
Here, we have taken the NASA Battery Dataset study that employs machine learning models, such as linear regression, decision tree, random forest, and support vector regression, to predict battery capacity based on features such as voltage, current, temperature, and time. Descriptive statistics, capacity vs. cycle plots, and capacity vs. state of health (SoH) plots are used to analyze the dataset and visualize the battery performance over time. The correlation between different features is examined using a correlation heatmap. The mean squared error (MSE), root mean squared error (RMSE), and high R2 scores are also calculated.
