Insights into ultrasound-promoted degradation of naphthenic acid compounds in oil sands process affected water. Part II: In silico quantum screening of hydroxyl radical initiated and propagated degradation of benzoic acid
Bibliographic record
Abstract
In Part I, we outlined the importance of sustainable sonochemical treatment to intensify oil sands process affected water (OSPW) treatment empirically and hypothesized degradation pathways. Herein, we elucidate the formation of intermediate products with well-defined molecular level solutions. Proposed mechanisms describe hydroxylation, decarboxylation and bond scission which drive the degradation of intermediates towards mineralization. This comprehensive first study on in silico screening of sonochemical degradation investigates quantum methods using density functional theory to explain the postulated degradation mechanisms through a theoretical radical attack approach, based on condensed Fukui reactivity indicators. A nudged elasticity band (NEB) approach is applied to find a minimum energy path (MEP), allowing the determination of intermediate products and energy barriers associated with naphthenic acid degradation. This approach provides structures and energies of the breakdown compounds formed along the reaction pathway enabling the determination of molecular reaction kinetics. In continuation of Part 1, the focus of this study is to evaluate sonochemically-induced hydroxyl radical (OH•) reactions of benzoic acid using density functional theory. Hydroxylation and decarboxylation mechanisms of the model naphthenic acid compound and its intermediates were simulated to determine the prospective pathway to ideal mineralization. DFT was applied to calculate interaction energies, Mulliken charges, Hirshfeld population analysis, dipole moments, frontier orbitals, and polarizability. Electronic properties and frontier orbital trends were also compared to computational work by Riahi et al. [1] to confirm the transition states by Nudged Elastic Band Transition State theory (NEB-TS). In combination with Hirshfeld Population analysis, Fukui indices suggest a more linear degradation pathway narrowed down from earlier experimental work by Singla et al. [2]. The linear free energy relationship for the newly suggested computational benzoic acid degradation can be determined by lnkTST/W=-1.677ΔG-15.41 with a R2 of 0.9997 according to classic transition state theory and Wigner tunneling. This computational method can be used to explore possible degradation pathways of other NAs and bridges molecular-to-macroscale sonochemical degradation of NA’s through a manifestation of molecular solutions.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".