Influence of Operating Conditions on Graphene Properties Synthesized from Two-Step Electrochemical Exfoliation
Bibliographic record
Abstract
Effective exfoliation of relatively high-quality graphene with good yield from graphite is critical for its extensive exploitation in several emerging technologies. Two-step electrochemical exfoliation of graphene offers a route towards achieving such high-quality graphene and tailoring of its properties for nanotechnology-related applications. A precise control of the intercalation and exfoliation conditions provides an additional dimension for tuning these graphene properties. Here, we report a systematic evaluation of the nature of defects induced in graphene with respect to varying intercalation times and intercalant composition using an acid-blend containing sulphuric acid (H2SO4) and phosphoric acid (H3PO4). A few-layer, exfoliated graphene (eG) is obtained by incorporating relevant functional groups in the main matrix during the intercalation step. Structural analysis by Raman and X-ray microscopy indicates a multi-atom-doped graphene with primarily “boundary -type” defect signature and n-type charge carriers synthesized for an optimized intercalation time of 1600 s. The average ratio of the defect-activated modes (I D / I G and I D / I D′ ) showed a unique defect feature at 1600 s irrespective of the intercalating electrolyte composition. This indicates the need for careful selection of intercalation times to control the nature of defects. The phosphorus and nitrogen-doping content in the eG increased with increasing phosphoric acid content which influences the thermal and electrical conductivity respectively. Most interesting, by using various intercalation times, we show that increasing intercalation times in the presence of high phosphoric acid content beyond 1600 s results in deteriorating electrical conductivity but enhanced thermal stability. A trade-off in conductivity and thermal stability shows competing factors influence these properties. We attribute this to loss of the sp2 graphitic structure at higher intercalation times which dominates over any contributions from the dopants. The highest electrical conductivity of ~14,000 S m-1 was recorded which is a two orders magnitude increase from the eG intercalated in pure H2SO4. The energy consumption (kWh kg-1) required for exfoliation was reduced by up to 50% and the exfoliation efficiency was enhanced with the incorporation of phosphoric acid into the intercalating electrolyte blend. This study thus provides a facile and energy-efficient recipe for synthesizing graphene nanostructures with unique defects relevant for use in specific applications related to energy storage and water treatment. Figure 1
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 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".