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Record W6925986438 · doi:10.20381/ruor-28116

Swelling, Thermal, and Hydraulic Properties of a Bentonite-Sand Barrier in a Deep Geological Repository for Radioactive Wastes: Effect of Groundwater Chemistry, Temperature and Physical Factors

2022· article· en· W6925986438 on OpenAlexaboutno aff

Bibliographic record

VenueuO Research (University of Ottawa) · 2022
Typearticle
Languageen
FieldMedicine
TopicMicrobial Natural Products and Biosynthesis
Canadian institutionsnot available
Fundersnot available
KeywordsRadioactive wasteBentoniteNuclear powerGroundwaterHydraulic conductivitySedimentary rockWaste disposalHigh-level wasteSpent nuclear fuel

Abstract

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Electricity generation at nuclear power plants produces a large amount of high-level radioactive waste (HLW) every year, which has long-term detrimental effects on humans and the environment. Other applications of nuclear technology (e.g., medicine, research, nuclear weapons, industry) also produce radioactive waste (e.g., low-level radioactive waste, LLW, Intermediate-level waste, ILW). The potential of deep geological repositories (DGRs) as an option for disposal of radioactive waste (HLW, ILW, LLW) has been examined in several countries, including Bulgaria, Canada, China, Finland, France, Germany, India, Japan, Russia, Spain, Sweden, Switzerland, Ukraine and the United Kingdom and are still under discussion. In Ontario, Canada, DGRs with a multi-barrier system comprised of a sedimentary rock formation (i.e., a natural barrier) and an engineered barrier system (EBS) are currently under consideration. An EBS consists of various components, such as waste containers, buffer, backfill, and tunnel sealing materials, intended to prevent the release of radionuclides. Several engineered barrier materials, including a mixture of bentonite and sand, are currently being considered for use in DGRs for nuclear waste in Ontario. Bentonite has some advantageous physical and chemical properties, such as low permeability, high plasticity, and high swelling potential, which provide it with a good sealing ability and thus make it an effective barrier. However, interaction between the compacted bentonite–sand mixture and underground water chemistry fluids (chemical factor) in the DGR could significantly alter the favourable properties of bentonite (e.g., swelling potential), thus influencing its performance when used in an EBS and eventually jeopardizing the overall safety of DGRs. In addition, other parameters, such as the clay content, initial dry density and moisture content of the compacted barrier (physical factors), as well as the presence of salts in groundwater may affect the physical and physiochemical properties of barrier materials. Moreover, during the lifetime of a DGR for used spent fuel, the bentonite–based barrier material will not only be exposed to a broad range of groundwaters with different chemical compositions, but also to high temperatures (heat generated by the nuclear wastes) (thermal factor). Thus, the interaction between the compacted bentonite–sand mixture, the surrounding groundwater and the heat from the nuclear waste material could jeopardize the favourable properties of the bentonite-based (bentonite-sand) barrier material. Properties of a bentonite-sand barrier is an important characteristic to study while designing and constructing an EBS for a DGR. Thus, to understand and assess the operations of DGRs in Ontario, comprehensive studies must be performed on engineering properties like swelling behaviour, permeability, and thermal conductivity. The goal of this research study is to experimentally investigate the physical, chemical and thermal factors that influencing the engineering properties of a barrier material made up of bentonite-sand composite used in DGRs for nuclear waste in Ontario. Compacted samples are subjected to one-dimensional free swell test to understand the swelling behaviour of the material. Hydraulic conductivity was investigated using a flexible wall permeability test. Thermal conductivity and diffusivity were tested using Decangon KD2 Pro with TR-1 and and KS-1 sensors. The specimens contain different bentonite–sand mixture ratios (20:80, 30:70, 50:50, and 70:30 dry mass), and they are tested under conditions with differing bentonite content, dry density, groundwater chemistry, and temperature. Additional tests were conducted to investigate the microstructure of the specimens. These tests include X-ray diffraction (XRD) analysis, mercury intrusion porosimetry (MIP), and thermogravimetric analyses (TG/DTG). The results reveal that the time and strain required to achieve maximum swelling of compacted bentonite–sand specimens increase with the increase of initial dry density. The simulated saline solutions of Guelph and Trenton groundwater are found to suppress the swelling of the bentonite–sand specimens. This in turn leads to the increase of hydraulic conductivity and decrease of thermal properties of the barrier material. However, the impact of the salinity is significantly reduced by increasing the dry densities and sand content of the compacted material. Moreover, the coupled effect of salinity and temperature decreases the swelling potential of the bentonite-sand mixture. Also, some transformation of Na-montmorillonite into Ca-Montmorillonite was observed. The results also indicate that some montmorillonites might have been transformed into illites, thereby further decreasing the swelling potential of the bentonite-based barrier.

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 score0.009
Threshold uncertainty score0.017

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 head0.020
GPT teacher head0.245
Teacher spread0.226 · 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".

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Citations0
Published2022
Admission routes1
Has abstractyes

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