Automated method for determining the flow of surface functionalized nanoparticles through a hydraulically fractured mineral formation using plasmonic silver nanoparticles
Bibliographic record
Abstract
Quantifying nanoparticle (NP) transport within porous geological media is imperative in the design of tracers and sensors to monitor the environmental impact of hydraulic fracturing that has seen increasing concern over recent years, in particular the potential pollution and contamination of aquifers. The surface chemistry of a NP defining many of its solubility and transport properties means that there is a wide range of functionality that it is desirable to screen for optimum transport. Most prior transport methods are limited in determining if significant adsorption occurs of a NP over a limited column distance, however, translating this to effects over large distances is difficult. Herein we report an automated method that allows for the simulation of adsorption effects of a dilute nanoparticle solution over large distances under a range of solution parameters. Using plasmonic silver NPs and UV-visible spectroscopic detection allows for low concentrations to be used while offering greater consistency in peak absorbance leading to a higher degree of data reliability and statistics. As an example, breakthrough curves were determined for mercaptosuccinic acid (MSA) and cysteamine (CYS) functionalized Ag NPs passing through Ottawa sand (typical proppant material) immobile phase (C) or bypassing the immobile phase (C0). Automation allows for multiple sequences such that the absorption plateau after each breakthrough and the rate of breakthrough can be compared for multiple runs to provide statistical analysis. The mobility of the NPs as a function of pH is readily determined. The stickiness (α) of the NP to the immobile phase calculated from the C/C0 ratio shows that MSA-Ag NPs show good mobility, with a slight decrease around neutral pH, while CYS-Ag NPs shows an almost sinusoidal variation. The automated process described herein allows for rapid screening of NP functionality, as a function of immobile phase (proppant versus reservoir material), hydraulic fracturing fluid additives (guar, surfactant) and conditions (pH, temperature).
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".