Intermolecular and Surface Interactions in Engineering Processes
Bibliographic record
Abstract
Interactions involving chemical reagents, solid particles, gas bubbles, liquid droplets, and solid surfaces in complex fluids play a vital role in many engineering processes, such as froth flotation, emulsion and foam formation, adsorption, and fouling and anti-fouling phenomena. These interactions at the molecular, nano-, and micro scale significantly influence and determine the macroscopic performance and efficiency of related engineering processes. Understanding the intermolecular and surface interactions in engineering processes is of both fundamental and practical importance, which not only improves production technologies, but also provides valuable insights into the development of new materials. In this review, the typical intermolecular and surface interactions involved in various engineering processes, including Derjaguin–Landau–Verwey–Overbeek (DLVO) interactions (i.e., van der Waals and electrical double-layer interactions) and non-DLVO interactions, such as steric and hydrophobic interactions, are first introduced. Nanomechanical techniques such as atomic force microscopy and surface forces apparatus for quantifying the interaction forces of molecules and surfaces in complex fluids are briefly introduced. Our recent progress on characterizing the intermolecular and surface interactions in several engineering systems are reviewed, including mineral flotation, petroleum engineering, wastewater treatment, and energy storage materials. The correlation of these fundamental interaction mechanisms with practical applications in resolving engineering challenges and the perspectives of the research field have also been discussed.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".