MétaCan
Menu
Back to cohort
Record W3111513251 · doi:10.1002/adsu.202000231

Assessing the Applicability of Gravity Separation for Recycling of Non‐Metal Fraction from Waste Printed Circuit Boards

2020· article· en· W3111513251 on OpenAlexaff
Amit Kumar, Maria Holuszko, Travis Janke

Bibliographic record

VenueAdvanced Sustainable Systems · 2020
Typearticle
Languageen
FieldEnvironmental Science
TopicRecycling and Waste Management Techniques
Canadian institutionsMembrane Reactor Technologies (Canada)
Fundersnot available
KeywordsPrinted circuit boardGravity separationFloat (project management)Specific gravityWaste managementFraction (chemistry)Environmental scienceTailingsMaterials scienceProcess engineeringEnvironmental engineeringEngineeringMetallurgyChemistryComposite materialElectrical engineeringChromatography

Abstract

fetched live from OpenAlex

Abstract Printed circuit boards (PCB) are one of the most studied electronic waste streams due to the presence of high‐value metals. The rejects from waste PCB recycling, also known as a non‐metal fraction (NMF), are usually sent to landfill. This work explores the applicability of gravity separation, widely used in the mineral processing industry, to separate organic and inorganic components in the NMF. The float‐sink test is conducted to understand the gravity separation performance, and the results show that a 47% yield at 86% organic content with 70% combustible recovery can be obtained using dense media separation. The washability behavior of NMF is assessed using different washability indices standard in the coal industry; and the material is classified as difficult‐to‐clean. The float‐sink products show the concentration of organic stream in −1.8 g cm−3 range, fiberglass in +2.0–2.5 g cm−3 range, and metals in +2.5 g cm−3 range. The recovery of these components will facilitate the concept of the circular economy and promote sustainability.

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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
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 head0.019
GPT teacher head0.313
Teacher spread0.293 · 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
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueAdvanced Sustainable SystemsSame topicRecycling and Waste Management TechniquesFrench-language works237,207