Applications of carbon quantum dots in electrochemical energy storage devices
Abstract
Supercapacitors (SCs), including electric double-layer capacitors (EDLCs), pseudocapacitors, and hybrid capacitors, are esteemed for their high power density and attractive features such as robust safety, fast charging, low maintenance, and prolonged cycling lifespan, sparking significant interest. Carbon quantum dots (CQDs) are fluorescent nanomaterials with small size, broad excitation spectrum, stable fluorescence, and adjustable emission wavelengths. They are widely used in optoelectronics, medical diagnostics, and energy storage due to their biocompatibility, low toxicity, rich surface functional groups, abundant electron-hole pairs, large specific surface area, and tunable heteroatom doping. In this short review, we briefly discussed the advantages and disadvantages of bottom-up and top-down of CQD synthesis methods. The arc-discharge technique, laser ablation technique, plasma treatment, ultrasound synthesis technique, electrochemical technique, chemical exfoliation, and combustion are among the initial top-down approaches. The subsequent section delineates waste-derived and bottom-up methods, encompassing microwave synthesis, hydrothermal synthesis, thermal pyrolysis, and the metal-organic framework template-assisted technique. In addition, this short review focuses on the operational mechanism of supercapacitors, their properties, and the utilization of CQDs in supercapacitors.
Read the paper
Related studies
- Chaos and quantum thermalization
- The Dalton quantum chemistry program system
- Cord Blood Leptin and Adiponectin as Predictors of Adiposity in Children at 3 Years of Age: A Prospective Cohort Study
- Quantitative metabolomics by H-NMR and LC-MS/MS confirms altered metabolic pathways in diabetes
- Stable adiabatic quantum batteries
- A novel self-lipid antigen targets human T cells against CD1c(+) leukemias
Go deeper in Evidence Hub
Evidence Hub adds a study data sheet (design, sample size, evidence level, key numbers), an AI summary and a chat grounded in the paper's text.