Microwave-Assisted Synthesis of Graphene-Coated Mn-Doped Lithium Iron Phosphate (LFMP/G) for Electrochemical Capacitor
Bibliographic record
Abstract
Lithium Iron Phosphate (LFP) material is widely used to power commercial products such as torches, automotive vehicles and certain laptops. The material is relatively cheap, safe to use and longer life times than some of the other battery chemistry materials; however it suffers from poor electronic conductivity. In order to improve the electronic conductivity of LFP, the material can be doped with transition metals such as manganese (Mn) and coated with a carbon source such as graphene [1]. Various methods can be used to synthesize LFP materials with microwave synthesis being one of the examples [2]. Microwave irradiation allows for rapid synthesis. The experimental processes involved a one-pot design where all the starting materials were added together and underwent ultra-sonication, microwaving, washing, drying and annealing at 700°C. During the microwave irradiation process it was seen that crystalline LFP material was formed with a coating of graphene to form LFP/G and some of the material was doped with Mn to form LFMP/G. The LFP/G and doped LFMP/G material was made up into asymmetrical electrochemical capacitors (AEC). The LFP/G AEC had a much faster charge and discharge response time at 7mA relative to the LFMP/G AEC, however the LFMP/G AEC could be charged and discharged to higher voltages. The LFP/G AEC provided 1.43 mFcm -2 at a current density of 8.85 mAcm¯² over 30 h, whereas the LFMP/G provided 4.63 mFcm -2 at a current density of 12.4 mAcm¯² over 50 h. Both capacitors showed very little internal resistance once the testing was completed. In this presentation, we will discuss in detail our findings on the various microwave-based synthetic strategies adopted for the LFP/G and LFMP/G and their electrochemical properties for the development of efficient cathode materials for lithium ion batteries and asymmetric electrochemical capacitors. References (1) I. Bilecka, A. Hintennach, I. Djerdj, P. Novàk, M. Niederberger, J. Mater. Chem. 19 (2009) 5125. (2) D. Jugović, D. Uskoković, J. Power Sources 190 (2009) 538. Figure 1
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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 | 0.001 | 0.001 |
| 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".