Mechanistic Consideration of pH Effect on the Enrichment of Semiconducting SWCNTs by Conjugated Polymer Extraction
Bibliographic record
Abstract
Enrichment schemes providing high-purity semiconducting single-walled carbon nanotubes (sc-SWCNTs) will enable their implementation into high end and printed electronics. Conjugated polymer extraction (CPE) has been shown to be a very effective and scalable method to isolate sc-SWCNTs with purities >99.9%. However, this method is often plagued with variability, and the mechanism is not thoroughly understood. Herein, we probe the origins of selectivity in polyfluorene-assisted enrichment using poly(9,9-di- n -dodecylfluorene) (PFDD) and find that the affinity of the wrapping polymer to bind metallic (m-) and semiconducting SWCNTs is similar and may not contribute to the selectivity, but rather that oxygen-driven p-doping of the nanotubes and its dependence on the surface acidity may play a vital role for the selective dispersion of sc-SWCNT. The latter hypothesis is tested by titration experiments in which sodium hydroxide (NaOH) is used to neutralize the SWCNT surface, thus mitigating oxygen p-doping of SWCNTs that arises via the oxygen/water (O 2 /H 2 O) redox couple. The selectivity of the enrichment is completely lost when 1 equiv of NaOH per 70 SWCNT carbons is used. Oxygen doping is believed to be the driving force that triggers the aggregation of highly polarizable m-tubes. These novel observations stress that care must be taken during purification schemes prior to enrichment using CPE, which is strongly influenced by acid/base treatments, likely due to the pH dependence of p-doping via the O 2 /H 2 O redox process.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".