Interfacial Design of Advanced 2D Nanomaterials for Sustainable Electrochemical Energy Storage
Bibliographic record
Abstract
Background: Advanced 2D nanomaterials are of great interest in electrochemical energy storage as they exhibit outstanding conductivity, high surface area, and tunable interfacial properties. Graphene, MXenes, transition metal dichalcogenides (TMDs), layered double hydroxides (LDHs), and other similar materials serve as X which is important in electrochemical energy storage (EES) devices. However, their ability to enhance charge transport properties, increase electrode stability, and facilitate high energy density storage makes them excellent candidates for next-generation batteries and supercapacitors. Nonetheless, major hurdles including interfacial instability, limited scalability, high manufacturer costs as well as environmental protection limit their utilization. Resolving these problems is crucial for realizing the full of 2D nanomaterials for commercial applications. Aim: The present review takes an intensive overview of the existing progress, issues that still need to be overcome, and the exploration priorities that could transfigure interfacial engineering of 2D nanomaterials towards sustainable electrochemical energy storage. Understanding the effectiveness of various nanomaterials in nanocomposite storage devices relies on knowledge of their interfacial cross-correlation and their role in energy storage capacity; therefore, this study compiles the most widely used nanomaterials, their interfacial properties, energy storage performance, and identifies critical gaps in the research that need to be overcome to make nanocomposite storage devices more ubiquitous. Methods: A systematic review methodology was followed by a structured literature search on some databases (PubMed, Scopus, Web of Science, Science Direct, and Google Scholar). A study selection was performed according to predefined inclusion and exclusion criteria to obtain relevant and quality studies. Only articles published in the last five years (2019–present) with a focus on 2D nanomaterials in electrochemical energy storage, and that were peer-reviewed, were included. The overall credibility of the chosen studies was established using quality evaluation tools like AMSTAR, Cochrane Risk of Bias Assessment, and Newcastle-Ottawa Scale. Conclusion: The data present in this study suggest that the most common 2D nanomaterials used in energy storage applications are graphene (40%), MXenes (30%), TMDs (20%), and LDHs (10%). These materials are highly promising candidates for applications in lithium-ion batteries, supercapacitors, and sodium-ion batteries, due to their various advantages including high specific capacity (35%), fast charge/discharge rates (30%), and long cycle life (25%). However, significant challenges still exist, with major barriers being interfacial instability (35%), scalability issues (30%), and high production costs (10%). Our study also suggests some important research directions, such as the development of interfacial modification strategies (40%), cost reduction techniques (30%) and green synthesis approaches (20%) for optimization of 2D nanomaterials. Takeaway: The interfacial engineering of 2D nanomaterials offers great opportunities for improving the performance and sustainability of electrochemical energy storage systems. Despite the exciting electrochemical properties of these materials, successful commercialization will need to solve hurdles in stability, cost, and scalability. Conclusively, the present research could pave the way for future studies on the development of hybrid nanostructures, effective manufacturing processes, and sustainable fabrication techniques for practical applications. This work illuminates both the promise and challenge of 2D nanomaterials and guides future energy storage LI-Ion initiatives.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".