Optimization of the PDMS/biochar nanocomposite membranes using the response surface methodology
Bibliographic record
Abstract
Abstract To improve the separation performance of the polydimethylsiloxane (PDMS)/bark biochar (BB) nanocomposite membranes used for alcohol/water separation, the preparation conditions of these composite membranes were analyzed and optimized. In this study, we investigated the following preparation parameters: the BB pyrolysis temperature, the weight ratio of the silane coupling agent (KH-550) to bark biochar (BB), and the BB loading amount. The regression equations were established between these three preparation parameters and the final pervaporation (PV) performance characteristics of the composite membranes. The membranes performed the best under the following optimal preparation conditions: a BB pyrolysis temperature of 407°C; a silane coupling reagent/BB weight ratio of 0.86, and a BB loading amount of 3.36 wt%. According to the results of the regression analysis, a maximum permeation flux of 221.2 g·m −2 ·h −1 and a maximum selective factor of 21.3 was obtained when the feed temperature for the 5 wt% alcohol solution was set at 40°C.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 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".