Search NASA⌕ Search

DOE OSTI · 2511232

Engineered MXene quantum dots for micro-supercapacitors with excellent capacitive behaviors

Abstract

Micro-supercapacitors (MSCs) have drawn tremendous attention as promising candidates to power miniaturized portable/wearable electronics, but they still suffer from unsatisfactory electrochemical performance (e.g., insufficient energy density, mediocre rate capability), thus impeding their widespread applications. Here, a synergistic surface and structure engineering strategy achieved by downsizing to quantum dot scale, doping of heteroatoms, and introducing defects and functional groups is proposed to regulate the physicochemical properties of Ti 3 C 2 T x MXene. Encouragingly, the resulting MSCs based on defect-rich nitrogen-doped Ti 3 C 2 T x quantum dots (QDs) possess excellent electrochemical performance as demonstrated by large operating voltage (3.0 V in ionic liquid and 1.0 V in aqueous electrolyte), perfect rectangular CV shape even at 1000 V·s -1 , high volumetric capacitance of 33.1 F·cm -3 , and superior cycling stability after 10000 cycles. By employing experimental characterizations and density functional theory calculations, the remarkable performance of the MSCs is mainly due to the special chemical states as well as the unique surface and structural features of Ti 3 C 2 T x QDs, which offer abundant active sites, shorten ion diffusion pathways, promote ion/electron transports, and provide enhanced capacitance. Finally, this work provides a new strategy for the design of high-performance MSCs and a reference for the applications of MXene QDs in other energy-related fields.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Liu, Wenwen, Luo, Dan, Zhang, Maiwen, Chen, Jiangtao, Li, Matthew, Chen, Anna, Xi, Shibo, Yu, Aiping. 2024-01-24. Engineered MXene quantum dots for micro-supercapacitors with excellent capacitive behaviors. https://doi.org/10.1016/j.nanoen.2024.109332

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related reports

Large language models for transportation research: Methodologies, state of the art, and future opportunities

The rapid rise of large language models (LLMs) is transforming transportation research, with significant advancements emerging between 2023 and 2025, a period marked by the inception and swift growth of adopting and adapting LLMs for various transportation applications. Despite these significant advancements, however, a systematic review and synthesis of the existing literature remains lacking. This paper aims to fill this gap by providing a comprehensive review of the methodologies and applications of LLMs in transportation. We explore key applications, including autonomous driving, travel behavior prediction, and general transportation-related queries, alongside LLM methodologies such as zero- or few-shot learning, prompt engineering, and fine-tuning. From the review, critical research gaps are identified. From the methodological perspective, many of the research limitations can be addressed by integrating LLMs with existing tools and refining LLM architectures. From the application perspective, research opportunities for LLMs to address various transportation challenges are also explored. By synthesizing these findings, this review not only presents the state-of-the-art LLM adoption and adaptation in transportation, but also proposes future research directions as well as insights and recommendations for policymakers and practitioners, paving the way for greater LLM-driven research innovations in transportation in the future.

42 ENGINEERING↗