Boosting the Performance of Aqueous Ammonium-Ion Batteries by Mitigating Side Reactions Using Polymer Additive
Bibliographic record
Abstract
Aqueous ammonium ion battery (AAIB) is a sustainable and highly safer energy storage technology than traditional metal-ion batteries owing to the low-cost, good diffusion kinetics, and abundant charge carrier ability. Besides, AAIBs suffer from less-cyclic stability to meet the practical applications due to the undesired side reactions and low electrochemical stable potential window. Herein, for the first time, the role of a molecular crowding agent, i.e., poly(ethylene oxide) (PEO) as an organic polymer-based electrolyte additive was tested to achieve high-performance AAIBs. The addition of PEO molecules improves the ammonium ion (NH 4 + ) kinetics and regulates the hydrogen bond behavior in the water through the interactions between oxygen (−O) groups in the ethylene oxide units of PEO and water. Such a strong interaction between the PEO–water network effectively suppresses hydrogen (HER) and oxygen evolution reactions (OER) and increases the potential window. Further, the weak interaction between PEO–NH 4 + facilitates the topotactic binding mechanism and eventually leads to increased ionic conductivity. In addition, the full cell is fabricated using 3,4,9,10-perylenetetracarboxylic dianhydride (PTCDA) as an anode and ammoniated nickel hexacyanoferrate (N-NiHCF) as a cathode. The assembled device shows a maximum capacity of 42.51 mAh/g at 0.3 A/g with a rate capability of 99.7%. The device shows negligible performance deterioration after 1000 charge–discharge cycles. Hence, this strategy sheds light on the effective utilization of polymer additives for the design and development of highly stable and sustainable ammonium ion batteries for stationary grid-scale applications.
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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 | 0.000 | 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 | 0.001 |
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".