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Phage Display - A Promising Tool for the Recovery of Valuable Metals from Primary and Secondary Resources

2017· article· en· W2747458472 on OpenAlexaff
Sabine Matys, Franziska Lederer, Nora Schönberger, Robert Braun, Falk Lehmann, Katrin Flemming, Stefanie Bachmann, Susan B. Curtis, Ross T. A. MacGillivray, Katrin Pollmann

Bibliographic record

VenueDiffusion and defect data, solid state data. Part B, Solid state phenomena/Solid state phenomena · 2017
Typearticle
Languageen
FieldMaterials Science
TopicDiatoms and Algae Research
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsPhage displayMaterials scienceNanotechnologyScrapBiochemical engineeringMetalLeaching (pedology)Combinatorial chemistryBiologyMetallurgyPeptideChemistryBiochemistryEngineering

Abstract

fetched live from OpenAlex

The development of effective and ecofriendly processes for the recovery of critical elements poses a challenge for scientists all over the world. A novel approach is the generation of highly specific peptides that bind with high affinity to individual elements of interest. The peptides are selected by phage surface display (PSD) technology. In this study, PSD technology has been applied in two different approaches. The focus of the first approach was the identification of peptides that bind specifically to special particles of interest that are part of electronic scrap aiming towards the development of new recycling processes. In the second approach, metal ion binding peptides were isolated via PSD to use them for the targeted removal and enrichment of these elements from complex leaching solutions or from industrial waters. To address the economic production of peptides, the development of a new expression system is also part of this study.

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 score0.005

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 communication0.0010.000
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 head0.049
GPT teacher head0.328
Teacher spread0.278 · 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

Citations5
Published2017
Admission routes1
Has abstractyes

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Same venueDiffusion and defect data, solid state data. Part B, Solid state phenomena/Solid state phenomenaSame topicDiatoms and Algae ResearchFrench-language works237,207