Investigating the hydrophilicity of phenol formaldehyde foams: Effects of synthesis parameters
Bibliographic record
Abstract
Abstract Open‐cell phenol (P) formaldehyde (F) foams are used for various applications such as hydroponic seed germination, flower arrangements, and sound insulation. This study investigated the influence of various parameters such as F/P molar ratios, blowing agents, surfactants, wetting agents, polymeric and inorganic additives, and oxidizing agents on the hydrophilicity of PF foams. Experimental results indicate that an F/P ratio of 1.8 was ideal for producing phenolic foams with good foam structure, uniform pore distribution, and favorable mechanical properties. A mixture of pentane and hexane at a mass ratio of 1:3 (wt/wt) used as a blowing agent resulted in a foam with the lowest density. With the optimal foaming catalyst (p‐toluene sulfonic acid) at a loading of 28 wt%, the foam exhibited a low density and excellent mechanical properties. A phenolic foam with a low density (39.27 kg/m3), high water absorption capacity (1210 wt%) and high open cell content (78.94%), was achieved by combining 6 wt% Tween‐80 as the surfactant and 3 wt% sodium dodecyl sulfate as the wetting agent. Additionally, 6.5 wt% loading of H2O2 led to a slight decrease in the foam density (38.46 kg/m3), but with a remarkable improvement in the wetting characteristics (water absorption capacity—2404 wt% and nearly 100% open cell content of the produced foam.
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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".