Constructing a 4 Volt Aqueous Lithium Ion Battery Using Acrylate-Based Gel Electrolytes
Bibliographic record
Abstract
Highly concentrated solutions of lithium salts in water have made sweeping strides from the time in the early 2010s where aqueous electrolytes could operate a battery within an electrochemical window no more than 1.5V wide. In this presentation, we discuss the construction of a lithium ion battery using graphite as the anode and LiCoO2 as the cathode to make a cell with a 4.2V potential. The primary electrolyte is a water:trimethylphosphate hybrid with a water mole fraction of 0.44 and LiTFSI salt at a concentration of 9 molal. This aqueous hybrid electrolyte can be formed into a gel electrolyte by directly polymerizing acrylate-based monomers and crosslinkers dissolved in the electrolyte. We demonstrate that by protecting the graphite anode using an acrylate gel with a fluoroethylene carbonate-based liquid electrolyte, the battery cell can be cycled repeatedly between 3.0V and 4.2V just like a cell using organic carbonate electrolytes. The advantage of the aqueous hybrid electrolyte is that it is non-flammable, and a cell using aqueous gel electrolytes can withstand damage and even be cut open while operating with no risk of fire or explosion. The manufacturing and performance characteristics of the aqueous 4V battery will be discussed as well as the interfacial issues that come about with the use of aqueous gel electrolytes in a 4V-capable battery system.
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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".