Gold Core Nanoparticle Mimics for Asphaltene Behaviors in Solution and at Interfaces
Bibliographic record
Abstract
Asphaltenes are a poorly defined class of self-assembling and surface active molecules present in crude oils. The nature and structure of the nanoaggregates they form remain subjects of debate and speculation. In this exploratory work, the surface properties of asphaltene nanoaggregates are probed using electrically neutral 5 nm diameter gold-core nanoparticles with alkyl, aromatic, and alkanol functionalities on their surfaces. These custom synthesized nanoparticles are characterized, and their enthalpies of solution at near infinite dilution and the interfacial tensions of solutions containing these nanoparticles are compared with the corresponding values for Athabasca pentane asphaltenes. The enthalpies of solution of these asphaltenes in toluene, heptane, pyridine, ethanol, and water are consistent with the behavior of gold-alkyl nanoparticles. The interfacial tension values of these asphaltenes at toluene–water and (toluene + heptane)–water interfaces are consistent with the behavior of gold-biphenyl nanoparticles as are the tendencies for these asphaltenes and gold-biphenyl nanoparticles to “precipitate” in toluene + heptane mixtures. Gold-alkyl nanoparticles are minimally surface active at toluene–water and (toluene + heptane)–water interfaces and remain dispersed in all toluene + heptane mixtures. The behavior of these asphaltenes in solution and at interfaces is inconsistent with the behavior of gold- n -alkanol nanoparticles. The outcomes of this formative work indicate potential roles for aromatic submolecular motifs on aggregate surfaces as a basis for interpreting asphaltene nanoparticle flocculation and interfacial properties, while alkyl submolecular motifs on aggregate surfaces appear to provide a basis for interpreting other aspects of asphaltene solution behavior. A number of lines of inquiry for future work are suggested.
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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".