Covalent Grafting of Chitosan onto Stainless Steel through Aryldiazonium Self-Adhesive Layers
Bibliographic record
Abstract
Although the conventional methods for strong attachment of chitosan onto stainless steel require many steps in different solvents, it has been demonstrated in this work that covalent grafting of chitosan on a steel surface can be easily achieved through the formation of a self-adhesive surface based on aryldiazonium seed layers. Initially, a polyaminophenyl layer is grafted on a stainless steel surface by means of the one-step GraftFast(TM) process (diazonium induced anchoring process). The grafted aminophenyl groups are then converted to an aryldiazonium seed layer by simply dipping the substrate in a sodium nitrite acidic solution. That diazonium-rich grafted layer can be used as a self-adhesive surface for subsequent spontaneous coating of chitosan onto the steel surface. X-ray photoelectron and impedance electrochemical spectroscopies were used to characterize the pristine and modified steel samples. As evidenced from impedance and linear polarization results, the primary polyaminophenyl layer characterized by a high charge transfer resistance contributed to better protection against corrosion of the resulting chitosan-coated steel in sulfuric acid medium.
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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".