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Record W4402651435 · doi:10.1051/e3sconf/202456911003

A study on the materials of smart barrier for selectively blocked TCE and TPH

2024· article· en· W4402651435 on OpenAlexaboutno aff
Jai-Young Lee, Seung‐Jin Oh, Minah Oh, Woori Cho, Hyewon Park, Jeonghyeon Lee, Su Hee Kim, Sanguon Jeong, Jinman Chang

Bibliographic record

VenueE3S Web of Conferences · 2024
Typearticle
Languageen
FieldComputer Science
TopicCloud Data Security Solutions
Canadian institutionsnot available
FundersDivision of Human Resource DevelopmentMinistry of Environment
KeywordsChemistryEnvironmental chemistryEnvironmental science

Abstract

fetched live from OpenAlex

Many barrier materials are used around industries, construction areas, livestock dump sites, waste landfills, and underground oil storage tanks. In the case of barriers, even if there is no inflow of contaminants, they do not provide selective permeability, develop water barrier properties, and hinder groundwater flow when contaminants encounter groundwater. Recently, contaminated sites that are difficult to resolve with current purification technologies continue to emerge. Pollutants flow into groundwater through the advection and diffusion by leaking. The pollutants introduced in this way cause pollution to the surrounding environment, and various types of severe underground environmental pollution problems occur depending on the type of pollutants leaked. Therefore, using a material that has the property of adsorbing organic contaminants and gelling them is harmless to the environment by penetrating groundwater under normal conditions. It has the property of selectively adsorbing the pollutant and gelling it upon contact with it underground. Therefore, the authors aim to study the applicability of polynorbornene and polyolefin as components of smart barrier materials that make barrier materials impermeable through the adsorption of pollutants, swelling, and coagulation behavior in this study. The components of smart barrier materials include Ottawa sand, organo-bentonite, polynorbornene, and polyolefin. Polynorbornene and polyolefin adsorb only organic pollutants selectively. Before applying polynorbornene and polyolefin to the barrier material, TCLP was performed to evaluate environmental hazards. As a result of heavy metal analysis, it was determined that there was no environmental hazard. The pH results are 7.27 for polynorbornene and 7.31 for polyolefin, indicating that both materials are slightly alkaline. In addition, as a result of testing with TCE (stock solution) and TPH (diesel crude oil) to confirm the swelling effect when in contact with organic pollutants, the efficiency of pollutant adsorption and swelling was found to be high when the ratio of polynorbornene: polyolefin = 6:4. Therefore, when using the material used in this study, it is expected that it can be applied as a component of a smart barrier material that selectively blocks pollutants.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.044
GPT teacher head0.294
Teacher spread0.250 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2024
Admission routes1
Has abstractyes

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