Structural Study of Maya Blue: Textural, Thermal and Solid-State Multinuclear Magnetic Resonance Characterization of the Palygorskite-Indigo and Sepiolite-Indigo Adducts
Bibliographic record
Abstract
Abstract Palygorskite-indigo and sepiolite-indigo adducts (2 wt.% indigo) were prepared by crushing the two compounds together in a mortar and heating the resulting mixtures at 150 and 120°C, respectively, for 20 h. The samples were tested chemically to ensure that they displayed the characteristic properties of Maya Blue. Textural analysis revealed that no apparent changes in microporosity occurred in sepiolite or palygorskite after thermal treatment at 120°C (sepiolite) and 150°C (palygorskite) for 20 h. Micropore measurements showed a loss of microporosity in both sepiolite and palygorskite after reaction with indigo. The TGA-DTG curves of the sepiolite-indigo and palygorskite-indigo adducts were similar to their pure clay mineral counterparts except for an additional weight loss at ∼360°C due to indigo. The 29 Si CP/MAS-NMR spectrum of the heated sepiolite-indigo adduct is very reminiscent of the spectrum of dehydrated sepiolite. Crushing indigo and sepiolite together initiates a complexation, clearly seen in the 13 C CP/MAS-NMR spectrum, which can be driven to completion by heat application. In contrast to the broad peaks of the pure indigo 13 C CP/MAS-NMR spectrum, the sepiolite-indigo adduct spectrum consists of a well-defined series of six narrow peaks in the 120.0–125.0 ppm range. In addition, the sepiolite-indigo spectrum has two narrow, shifted peaks corresponding to the carbonyl group and the C-7 (C-16) of indigo. A model is proposed in which indigo molecules are rigidly fixed to the clay mineral surface through hydrogen bonds with edge silanol groups, and these molecules act to block the nano-tunnel entrances.
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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".