Aluminum pigments encapsulated with hybrid silica film with carboxyl groups and their stability and dispersibility in aqueous media
Bibliographic record
Abstract
Aluminum pigments encapsulated with hybrid SiO2 film with carboxyl (–COOH) groups were prepared through a sol‐gel process of tetraethoxysilane (TEOS) and vinyl triethoxysilane (VTES), followed by radical copolymerization of methyl methacrylate (MMA) and acrylic acid (AA) with the vinyl group of VTES. The composite aluminum pigments were characterized by means of Fourier transform infrared spectroscopy (FTIR), scanning electron microscopy (SEM), and X‐ray photoelectron spectroscopy (XPS). Their stability in acid media as well as dispersibility in water were also evaluated. It was found that VTES and TEOS could simultaneously hydrolyze and condense with hydroxyl groups on the surface of aluminum particles to form inorganic‐organic hybrid films, and copolymerization of MMA and AA could provide the film surface with –COOH groups. This helps the composite aluminum pigments disperse better in water than raw aluminum pigments (raw Al) counterparts. Compared with the serious corrosion problem of raw Al, which generated about 113 mL H2 in acid media of pH=1 in 30 days, the composite aluminum pigments showed much better anticorrosion performance (with only about 0.5 mL H2). The waterborne coatings made with this composite aluminum pigments showed great improvements on adhesive performance and stability in 0.1 mol/L H2SO4 or NaOH media.
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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) | 0.000 | 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".