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Record W2315106245 · doi:10.2172/1116552

Preparation of High Purity, High Molecular-Weight Chitin from Ionic Liquids for Use as an Adsorbate for the Extraction of Uranium from Seawater

2013· report· en· W2315106245 on OpenAlexaboutno aff
Robin D. Rogers

Bibliographic record

Venuenot available
Typereport
Languageen
FieldSocial Sciences
TopicForensic Fingerprint Detection Methods
Canadian institutionsnot available
Fundersnot available
KeywordsUraniumChitinSeawaterExtraction (chemistry)NanofiberIonic liquidRaw materialChemical engineeringChemistryEnvironmental sciencePulp and paper industryWaste managementNanotechnologyMaterials scienceChromatographyMetallurgyOrganic chemistryGeologyChitosanEngineeringCatalysisOceanography

Abstract

fetched live from OpenAlex

Ensuring a domestic supply of uranium is a key issue facing the wider implementation of nuclear power. Uranium is mostly mined in Kazakhstan, Australia, and Canada, and there are few high-grade uranium reserves left worldwide. Therefore, one of the most appealing potential sources of uranium is the vast quantity dissolved in the oceans (estimated to be 4.4 billion tons worldwide). There have been research efforts centered on finding a means to extract uranium from seawater for decades, but so far none have resulted in an economically viable product, due in part to the fact that the materials that have been successfully demonstrated to date are too costly (in terms of money and energy) to produce on the necessary scale. Ionic Liquids (salts which melt below 100{degrees}C) can completely dissolve raw crustacean shells, leading to recovery of a high purity, high molecular weight chitin powder and to fibers and films which can be spun directly from the extract solution suggesting that continuous processing might be feasible. The work proposed here will utilize the unprecedented control this makes possible over the chitin fiber a) to prepare electrospun nanofibers of very high surface area and in specific architectures, b) to modify the fiber surfaces chemically with selective extractant capacity, and c) to demonstrate their utility in the direct extraction and recovery of uranium from seawater. This approach will 1) provide direct extraction of chitin from shellfish waste thus saving energy over the current industrial process for obtaining chitin; 2) allow continuous processing of nanofibers for very high surface area fibers in an economical operation; 3) provide a unique high molecular weight chitin not available from the current industrial process, leading to stronger, more durable fibers; and 4) allow easy chemical modification of the large surface areas of the fibers for appending uranyl selective functionality providing selectivity and ease of stripping. The resulting sorbent should prove economically feasible, as well as providing an overall net energy gain.

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 integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.001

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.

Opus teacher head0.052
GPT teacher head0.400
Teacher spread0.348 · 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

Citations1
Published2013
Admission routes1
Has abstractyes

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