Surface characteristics of chemically modified newsprint fibers determined by inverse gas chromatography
Bibliographic record
Abstract
The surface characteristics of treated waste newsprint fibers were investigated using inverse gas chromatography (IGC). The surfaces of waste newsprint fibers were modified with γ-aminopropyltrie-thoxysilane, dichlorodiethylsilane (DCS), phthalic anhydride (PA), and maleated polypropylene. The effectiveness of these surface treatments was monitored by the IGC adsorption curves using n-alkanes and acid-base probes. The empirical acid (KA) and base (KD) characteristics (i.e., electron donor/ acceptor abilities) of untreated and treated newsprint fibers were determined using Schultz's method and were correlated with the surface chemical compositions determined from X-ray photoelectron spectroscopy and Fourier transform infrared spectroscopy. The results indicated that the surface of untreated newsprint fibers had an acidic characteristic due to the electron acceptor character of the hydroxyl protons. The newsprint fibers reacted with phthalic anhydride or malcated polypropylene also exhibited an acidic surface behavior attributed to pendent carboxylic groups. Dichlorodiethylsilane produced a strong acidic surface attributed to the highly electronegative nature of the chlorine atoms of dichlorodiethylsilane. However, when the fibers were reacted with γ-aminopropyltriethoxysilane, the basic characteristic (electron donor ability) of the fiber surface was increased, presumably by the presence of attached amino groups.
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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".