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Record W2527487468 · doi:10.11159/icbb16.119

Biotechnologies for Metal Extraction: Assessment of Microalgae for Rare Earths Recycling and Environmental Remediation

2016· article· en· W2527487468 on OpenAlexvenueno aff
Pro Danièle, Vial Jérôme, Corinne Rivasseau

Bibliographic record

VenueProceedings of the World Congress on New Technologies · 2016
Typearticle
Languageen
FieldEngineering
TopicExtraction and Separation Processes
Canadian institutionsnot available
Fundersnot available
KeywordsEnvironmental remediationExtraction (chemistry)Environmental scienceWaste managementEnvironmental chemistryChemistryContaminationEngineeringEcologyChromatography

Abstract

fetched live from OpenAlex

Extended Abstract The rare earths elements are metallic elements of strategic importance due to their multiple applications in high technologies, electronics, green energy, the automobile industry, the defense industry, etc. They take an essential part in permanent magnets, rechargeable batteries, lamp phosphors, catalysts, or hard disk drivers. They have been increasingly used in the last years, with an annual rise in the demand reaching 30% for some of them. Only one country, China, ensures over 90% of the world supply. In the early 2010 ies , China strongly reduced its export quotas for rare earths, leading to a drastic increase in prices, damaging industries in other countries, and forcing producers of rare earth products to relocate their operations to China. Facing the increasing use of rare earths and their uncertain supply, developed countries have developed new strategies to ensure access to these critical materials, including their recycling from electronic waste, used fluorescent lamps, magnets, and batteries, as an example [1,2]. Rare earths are conventionally extracted by pyrometallurgy and hydrometallurgy, which are mainly used at present despite their cost in energy and chemicals. In many fields, more environmentally friendly biotechnological processes have proved their efficiency. In recent years, several studies have shown the potential of biotechnologies for the recovery of rare earths [3]. Over the past few years, we have been studying the properties of microalgae of the Coccomyxa genus which were isolated from an extreme environment [4]. These microalgae withstand radiative, nutrient and metallic stress [5] and also have non-selective metal uptake properties. This makes them organisms of choice for the development of bioprocesses for rare earth extraction from matrices presently considered as waste for their recycling as well as from contaminated environments for remediation purposes. This work assesses the potential of these microalgae for such processes. By using an experimental design, the impact of various parameters on metal uptake was studied, including the nature of the rare earth element, its initial concentration, its speciation, the exposure pH and the contact time. This original approach enables to assess a large panel of conditions while minimizing the number of experiments. Two types of responses were analyzed: the physiological state of microalgae during exposure and responses regarding the accumulation of the rare earth. The percentage of purification was analyzed for remediation purposes, and the amount of metal accumulated for recycling. This study highlighted operating conditions for which the percentage of rare earth decontamination and the rare earth amount accumulated by microalgae were maximized. Accumulation was compared for the different elements tested. More than 90% of the metal could be removed from water within a few hours, with a concentration factor of about 20,000 [6]. Up to 15 mg of rare earth/g dry weight could be taken up by microalgae. Studies relating the take up of rare earths by microalgae are not numerous, but the fixation capacity determined in this work was superior to the amount reported for another algal species [7]. This work demonstrated the potential of these microalgae for environmental remediation of rare earths and for their recycling from urban mining.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.015
GPT teacher head0.266
Teacher spread0.251 · 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

Citations2
Published2016
Admission routes1
Has abstractyes

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