Probing Electrode-Electrolyte Interfaces Using Nano-Gap Surface-Enhanced Raman Spectroscopy and Imaging
Bibliographic record
Abstract
Performance of portable electronic devices and electric vehicles is currently limited by the energy density, lifetime and safety of lithium rechargeable batteries. Overcoming those limitations requires in-depth understanding of the electrode-electrolyte interfaces. Surface-enhanced Raman spectroscopy (SERS) can provide a critical component to this effort as an in situ technique with ultrahigh surface sensitivity that provides quantitative molecular specificity. SERS relies on local electromagnetic field amplification generated by surface plasmon resonance (SPR) at the surface of metal nanoparticles (NPs) with sub-wavelength dimensions. The most significant Raman enhancements have been observed when the local SPRs of multiple NPs interact. This occurs when the NPs are in close proximity (less than 10 nm), which could be achieved in ordered NP monolayers if they could be assembled reproducibly. Methods to assemble gold NPs into large area (cm 2 ) monolayers will be presented. The interparticle gap can be tuned between 1 and 4 nm by using surface ligands of different sizes. The Au NP monolayers exhibit high SERS sensitivity (enhancement factor greater than 10 7 ). This allows for the measurement of electrolyte components across a broad range of concentrations. Components include lithium hexafluorophosphate (LiPF 6 ), fluoroethylene carbonate (FEC), ethylene carbonate (EC) and diethyl carbonate (DEC). The investigation of solutions of LiPF 6 in EC + DEC binary solvents using SERS allows for the determination of the solvent coordination numbers, which range from 2 to 5. This result is in sharp contrast to the bulk values calculated from infrared spectroscopy, which range from 3 to 7. Numerical simulations show that the electromagnetic field is concentrated in the nanogap, indicating the information of the aprotic electrolyte solution structures is obtained from the immediately adjacent area of the solid substrate. Our findings facilitate a better understanding of the structure of solvated ions in close proximity to the electrode surface. Acknowledgment This work is funded by Assistant Secretary for Energy Efficiency and Renewable Energy, Office of Vehicle Technologies of the U.S. Department of Energy. R.L. Sacci is supported by Transport (FIRST) Center, an Energy Frontier Research Center funded by the U.S. Department of Energy, Office of Science, Office of Basic Energy Sciences. Confocal Raman microscopy experiments were conducted at the Center for Nanophase Materials Sciences, which is a DOE Office of Science User Facility.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".