Surface and interface characterization of untreated and SMA Imide‐treated hemp fiber/acrylic composites
Bibliographic record
Abstract
Abstract The hydrophilic nature of natural fibers adversely affects adhesion to a hydrophobic matrix, and consequently it may unfavorably influence the strength of the composite. Therefore, modifying the fiber or the matrix is essential to obtain optimum composite properties. In this work, hemp fibers were modified applying a paper sizing technique using SMA Imide resin (copolymer of styrene and dimethylaminopropylamine maleimide) as a surface modifying agent. The performance of the hemp/acrylic composite was improved significantly using the treated fibers. Inverse gas chromatography (IGC) and pull‐out test were employed to study the hemp fiber/matrix interface and the surface characteristics of untreated and treated hemp fibers. The IGC results demonstrated that treated fibers had slightly higher dispersive force compared with untreated fibers. Moreover, modification of fibers with SMA Imide resin slightly decreased the basic character and significantly increased the acid character of hemp fibers. From the pull‐out test, the average stress to pull the SMA‐treated fibers out was 71% higher than that calculated for untreated fibers. The higher interfacial strength for the treated fibers shows that the SMA treatment had a beneficial influence on the adhesion of the acrylic resin to the hemp fibers. POLYM. COMPOS., 2009. © 2009 Society of Plastics Engineers
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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".