Synergetic effect of N/O functional groups and microstructures of activated carbon on supercapacitor performance by machine learning

2022 ◽  
Vol 521 ◽  
pp. 230968
Author(s):  
Mohammad Rahimi ◽  
Mohammad Hossein Abbaspour-Fard ◽  
Abbas Rohani
2017 ◽  
Vol 28 (Suppl. 1) ◽  
pp. 227-240 ◽  
Author(s):  
Nur 'Izzati A. Ghani ◽  
◽  
Nur Yusra Mt Yusuf ◽  
Wan Nor Roslam Wan Isahak ◽  
Mohd Shahbuddin Masdar ◽  
...  

2021 ◽  
Vol 16 (1) ◽  
Author(s):  
Gopal Krishna Gupta ◽  
Pinky Sagar ◽  
Sumit Kumar Pandey ◽  
Monika Srivastava ◽  
A. K. Singh ◽  
...  

AbstractHerein, we demonstrate the fabrication of highly capacitive activated carbon (AC) using a bio-waste Kusha grass (Desmostachya bipinnata), by employing a chemical process followed by activation through KOH. The as-synthesized few-layered activated carbon has been confirmed through X-ray powder diffraction, transmission electron microscopy, and Raman spectroscopy techniques. The chemical environment of the as-prepared sample has been accessed through FTIR and UV–visible spectroscopy. The surface area and porosity of the as-synthesized material have been accessed through the Brunauer–Emmett–Teller method. All the electrochemical measurements have been performed through cyclic voltammetry and galvanometric charging/discharging (GCD) method, but primarily, we focus on GCD due to the accuracy of the technique. Moreover, the as-synthesized AC material shows a maximum specific capacitance as 218 F g−1 in the potential window ranging from − 0.35 to + 0.45 V. Also, the AC exhibits an excellent energy density of ~ 19.3 Wh kg−1 and power density of ~ 277.92 W kg−1, respectively, in the same operating potential window. It has also shown very good capacitance retention capability even after 5000th cycles. The fabricated supercapacitor shows a good energy density and power density, respectively, and good retention in capacitance at remarkably higher charging/discharging rates with excellent cycling stability. Henceforth, bio-waste Kusha grass-derived activated carbon (DP-AC) shows good promise and can be applied in supercapacitor applications due to its outstanding electrochemical properties. Herein, we envision that our results illustrate a simple and innovative approach to synthesize a bio-waste Kusha grass-derived activated carbon (DP-AC) as an emerging supercapacitor electrode material and widen its practical application in electrochemical energy storage fields.


Fermentation ◽  
2021 ◽  
Vol 7 (3) ◽  
pp. 119
Author(s):  
Vasiliki Summerson ◽  
Claudia Gonzalez Viejo ◽  
Damir D. Torrico ◽  
Alexis Pang ◽  
Sigfredo Fuentes

The incidence and intensity of bushfires is increasing due to climate change, resulting in a greater risk of smoke taint development in wine. In this study, smoke-tainted and non-smoke-tainted wines were subjected to treatments using activated carbon with/without the addition of a cleaving enzyme treatment to hydrolyze glycoconjugates. Chemical measurements and volatile aroma compounds were assessed for each treatment, with the two smoke taint amelioration treatments exhibiting lower mean values for volatile aroma compounds exhibiting positive ‘fruit’ aromas. Furthermore, a low-cost electronic nose (e-nose) was used to assess the wines. A machine learning model based on artificial neural networks (ANN) was developed using the e-nose outputs from the unsmoked control wine, unsmoked wine with activated carbon treatment, unsmoked wine with a cleaving enzyme plus activated carbon treatment, and smoke-tainted control wine samples as inputs to classify the wines according to the smoke taint amelioration treatment. The model displayed a high overall accuracy of 98% in classifying the e-nose readings, illustrating it may be a rapid, cost-effective tool for winemakers to assess the effectiveness of smoke taint amelioration treatment by activated carbon with/without the use of a cleaving enzyme. Furthermore, the use of a cleaving enzyme coupled with activated carbon was found to be effective in ameliorating smoke taint in wine and may help delay the resurgence of smoke aromas in wine following the aging and hydrolysis of glycoconjugates.


2019 ◽  
Vol 212 ◽  
pp. 1210-1223 ◽  
Author(s):  
Wen Jiang ◽  
Xianjun Xing ◽  
Shan Li ◽  
Xianwen Zhang ◽  
Wenquan Wang

Carbon ◽  
2016 ◽  
Vol 107 ◽  
pp. 933
Author(s):  
Kei Kato ◽  
Takuya Yura ◽  
Kiyoharu Nakagawa ◽  
Hirokazu Oda

Author(s):  
Syed Ishtiyaq Ahmed ◽  
Sreevatsan Radhakrishnan ◽  
Binoy B Nair ◽  
Rajagopalan Thiruvengadathan

Abstract Recent years have witnessed the rise of supercapacitor as effective energy storage device. Specifically, carbon-based electrodes have been experimentally well studied and used in the fabrication of supercapacitors due to their excellent electrochemical properties. This work reports the development and utilization of highly tuned and efficient Machine Learning (ML) models that give insights into correlation between structural features of electrodes and supercapacitor performance metrics namely specific capacitance, power density and energy density. Artificial Neural Networks (ANN) and Random Forest (RF) models have been employed to predict the various in-operando performance metrics of carbon-based supercapacitors based on three input features such as mesopore surface area, micropore surface area and scan rate. Experimentally measured values of these parameters used for training and testing these two models have been extracted from a set of research papers reported in literature. The optimization techniques and various tuning methodologies adopted for identifying model hyperparameters are discussed in this paper. The authors demonstrate the importance of hyperparameter tuning and optimization in building accurate and reliable computational models.


2021 ◽  
Author(s):  
Abigail Enders ◽  
Nicole North ◽  
Chase Fensore ◽  
Juan Velez-Alvarez ◽  
Heather Allen

<p>Fourier Transform Infrared Spectroscopy (FTIR) is a ubiquitous spectroscopic technique. Spectral interpretation is a time-consuming process, but it yields important information about functional groups present in compounds and in complex substances. We develop a generalizable model via a machine learning (ML) algorithm using Convolutional Neural Networks (CNNs) to identify the presence of functional groups in gas phase FTIR spectra. The ML models will reduce the amount of time required to analyze functional groups and facilitate interpretation of FTIR spectra. Through web scraping, we acquire intensity-frequency data from 8728 gas phase organic molecules within the NIST spectral database and transform the data into images. We successfully train models for 15 of the most common organic functional groups, which we then determine via identification from previously untrained spectra. These models serve to expand the application of FTIR measurements for facile analysis of organic samples. Our approach was done such that we have broad functional group models that inference in tandem to provide full interpretation of a spectrum. We present the first implementation of ML using image-based CNNs for predicting functional groups from a spectroscopic method.</p>


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