Thermal Stability of Zinc Compounds
Bibliographic record
Abstract
Zn and Cl have been found in deposits in municipal solid waste (MSW) boilers and industrial boilers. This leads to the general belief that ZnCl 2 may play a role in corrosion of heat-transfer tubes, owing to its low melting temperature and high corrosivity. In this study, the thermal stability of the compounds ZnCl 2, ZnSO 4, and ZnO as well as mixtures of ZnCl 2 and NaCl/KCl was investigated by means of thermogravimetric analysis/differential scanning calorimetry (TGA/DSC). The reactions of the Zn compounds with SO 2 /SO 3 and HCl were also investigated. The results obtained show that ZnCl 2 melts at 320 °C. Above 400 °C, ZnCl 2 vaporizes and is partly oxidized to ZnO. ZnSO 4 is stable up to 680 °C, at which it decomposes and, subsequently, forms ZnO above 900 °C. ZnO is stable at a much higher temperatures but can be chlorinated to ZnCl 2 in the presence of HCl at temperatures around 300 °C. In the presence of a large amount of NaCl/KCl, which is typically the case in actual boilers, ZnCl 2 reacts with NaCl/KCl to form 2NaCl·ZnCl 2 and 2KCl·ZnCl 2, respectively. These compounds melt at temperatures lower than NaCl and KCl but higher than ZnCl 2 . The findings show the significance of understanding the thermal stability of various zinc compounds, how they interact with one another, and whether they react with alkali chlorides and other compounds in the deposits to assess the role of Zn in deposit formation and corrosion in boilers burning Zn-containing fuels.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.002 | 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".