Characterization of graphene-filled fluoropolymer coatings for condensing heat exchangers
Bibliographic record
Abstract
Condensing heat exchangers are thermal devices subjected to extremely corrosive environments due to the formation of acidic condensates on the heat-exchange elements during service. To protect the heat exchangers from chemical attack, perfluoroalkoxy (PFA) coating has been applied as a barrier layer onto the surfaces of the heat-exchange elements to prevent corrosion. However, PFA has intrinsically poor thermal conductivity, and low wear resistance; thus, it is not naturally a good material for heat exchanger application. In this study, graphene nanoplatelets (GNPs) are incorporated into PFA powder as coating materials to improve the thermal properties of the fluoropolymer, for condensing heat exchangers application. Two grades of GNPs (8 nm and 60 nm layer thickness) are tested to evaluate the effect of graphene addition on the thermal, adhesion, electrical, and wear properties of the composites, which are compared to those mixed with multi-walled carbon nanotubes (MWCNTs). The results showed that both grades of GNPs significantly increased the thermal conductivity, i.e., ∼8× that of the virgin PFA. The composites incorporated with both grades of GNPs also demonstrated good coating adhesion strength and wear resistance, as well as excellent corrosion resistance. The composite filled with MWCNTs exhibited poor surface finish and minimal improvement in thermal performance.
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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 | 0.000 |
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".