SpeciationDetectionDeciphering C<sub>2</sub>H<sub>2</sub>/Cl-Driven Mercury Escape Mechanismsin PVC Production
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Mercury emissions from acetylene hydrochlorination processes in polyvinyl chloride (PVC) production pose severe environmental risks yet remain poorly quantified due to methodological limitations in high-reactivity C<sub>2</sub>H<sub>2</sub>/HCl atmospheres. We reported an advanced operando mercury speciation platform named PVC-OHM, engineered through systematic optimization of the Ontario Hydro method (OHM), achieving simultaneous real-time detection of elemental mercury (Hg<sup>0</sup>) and divalent mercury (Hg<sup>2+</sup>) with unprecedented sensitivity (0.12 μg/m<sup>3</sup>) under industrially relevant conditions. Our mechanistic investigation reveals three dominant mercury escape pathways: (1) thermal desorption, wherein localized hotspots accelerate HgCl<sub>2</sub> decomposition and elevate the saturated vapor pressure at carbon-mercury interface (from 0.9 to 20.3 atm), contributed to 80% of total Hg loss; (2) C<sub>2</sub>H<sub>2</sub> driven reductive decomposition of HgCl<sub>2</sub> catalyst at carbon–mercury interfaces, responsible for 65–78% of Hg<sup>0</sup> emissions and (3) excess chlorine adsorption-induced desorption ([HgCl<sub>2</sub>/AC] Cl<sub>5</sub>, <i>E</i><sub>ads</sub> > 0) accounting for >10% of Hg<sup>2+</sup> emissions. Quantitative tracking demonstrates alarming mercury loss of per gram catalyst (5 wt % Hg) reached as 3.08 mg Hg<sup>0</sup> and 9.01 mg Hg<sup>2+</sup>, directly linked to localized thermal gradients (Δ<i>T</i> = 250–300 °C) at reaction hotspots. This work establishes the first experimentally validated framework for mercury fate prediction in carbide-based PVC synthesis, providing actionable strategies for emission control through hotspot mitigation and coordination environment optimization.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.029 | 0.013 |
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 it