Novel Films Based on Inexpensive and Readily Available Materials for Use As a Hydrocarbon Sensor
Bibliographic record
Abstract
One of the most commonly cited arguments against erecting and using pipelines to transport oil and gas, is that of the potential leaking of these compounds into the environment. These leaks can infiltrate water systems, kill wildlife and contaminate soil. In many instances, pipeline owners are unaware of leaks. In Alberta (Canada), it was found that, on average, it can take as long as 48 days to respond to and isolate a pipeline leak (according to the Alberta Energy Regulator)1. In Canada alone, there are an estimated 825,000 kilometers of pipeline infrastructure already in operation2. The depth of cover for pipelines varies, depending on where the pipeline is located, creating significant challenges when monitoring pipelines for leaks. Identifying gas seepages is significantly easier than liquid leaks, as gases rise up out of the ground. Liquids, on the other hand, seep down into the ground, compounding the issue. In this work, a novel fabrication process for a single use, low-cost liquid hydrocarbon sensor has been developed. The process is simple, and the materials are readily available and low cost. The film has controllable hydrophilicity and can present amphiphilic properties, as well as strong electrical conductivity. We have successfully applied this film as a sensor for the presence of hydrocarbons that could be found in the event of a leak from an oil pipeline. The sensor responds rapidly to alkanes of varying lengths and has also shown strong responses to both functionalized hydrocarbons and aromatics. The sensor is both inexpensive and easy to manufacture, with scale-up being a matter of simply using a larger container for the synthesis. The sensor does not respond to water, a potential interferent for a sensor placed in soil, and has shown good stability towards dessication and long-term storage. Combined, this supports the application of such a device for the field detection of organic compounds commonly found in the presence of an oil spill or pipeline leak.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".