Synthesis of Nickel Fumarate and Its Electrochemical Properties for Li-Ion Batteries
Bibliographic record
Abstract
Metal–organic frameworks (MOFs) have found a potential application in various domains such as gas storage/separation, drug delivery, catalysis, etc. Recently, they have found considerable attention for energy storage applications such as Li- and Na-ion batteries. However, the development of MOFs is plagued by their limited energy density that arises from high molecular weight and low volumetric density. The choice of ligand plays a crucial role in determining the performance of the MOFs. Here, we report a nickel-based one-dimensional metal-organic framework, NiC4H2O4, built from bidentate fumarate ligands for anode application in Li-ion batteries. The material was obtained by a simple chimie douce precipitation method using nickel acetate and fumaric acid. Moreover, a composite material of the MOF with reduced graphene oxide (rGO) was prepared to enhance the lithium storage performance as the rGO can enhance the electronic conductivity. Electrochemical lithium storage in the framework and the effect of rGO on the performance have been investigated by cyclic voltammetry, galvanostatic charge–discharge measurements, and EIS studies. The pristine nickel formate encounters serious capacity fading while the rGO composite offers good cycling stability with high reversible capacities of over 800 mAh g−1.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".