Experimental andTheoretical Study of the Kineticsof Dimerization of Ammonia at Low Temperatures
Bibliographic record
Abstract
The kinetics of the dimerization of NH<sub>3</sub> in helium and nitrogen bath gas within the supersonic flow of a Laval nozzle were investigated at very low temperatures. Experimentally, the fraction of the NH<sub>3</sub> monomer, <i>f</i><sub>monomer</sub>, remaining in the flow at 91 K in N<sub>2</sub> and at 35 K in He for a total bath gas density [M]∼5 × 10<sup>16</sup> molecules cm<sup>–3</sup> was monitored using fluorescence from the electronically excited NH<sub>2</sub> photofragment formed following NH<sub>3</sub> photolysis at 213 nm. No dimerization was observed up to [NH<sub>3</sub>] = 1 × 10<sup>15</sup> molecules cm<sup>–3</sup> for 160 mm downstream of the 91 K N<sub>2</sub> nozzle, nor up to [NH<sub>3</sub>] = 5 × 10<sup>14</sup> molecules cm<sup>–3</sup> for 150 mm downstream of the 35 K He nozzle. Dimerization was observed at higher [NH<sub>3</sub>], being more pronounced at lower temperatures. For the C<sub>s</sub> and C<sub>2h</sub> conformers of the NH<sub>3</sub> dimer, calculations at the CCSD(T)/aug-cc-pVTZ level gave a zero-point vibrational-energy corrected binding energy of −7.52 and −7.33 kJ mol<sup>–1</sup>, respectively. Energy-grained master equation calculations based on statistical rate theory using the open-source MESMER package were used to calculate rate coefficients for dimerization, <i>k</i><sub>dimer</sub>, over the temperature range <i>T</i> = 25–300 K and [M] = 10<sup>13</sup>–10<sup>22</sup> molecules cm<sup>–3</sup> for He and N<sub>2</sub>. <i>k</i><sub>dimer</sub> displayed a negative <i>T</i> dependence and a positive [M] dependence and was found to be sensitive to changes in the low-lying vibrational frequencies of the NH<sub>3</sub> dimer, for example, the inclusion of a hindered rotor potential for the internal twisting mode, which alters the density of states. Using the axial profiles of <i>T</i>, [M], and velocity for the Laval nozzles, the calculated values of <i>k</i><sub>dimer</sub> were used to calculate <i>f</i><sub>monomer</sub> in the flow for comparison with the experiment. At higher [NH<sub>3</sub>], when dimerization was observed, the calculations significantly underestimated the degree of dimerization taking place in the flow, with a significant increase in the calculated value of <i>k</i><sub>dimer</sub> required to match the experiment. The reasons for the discrepancy are discussed, for example, errors in the calculation of the density of states for the NH<sub>3</sub> dimer and the average energy removed per collision by the bath gas at very low temperatures.
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How this classification was reachedexpand
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.030 | 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 teacher head, 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".