MétaCan
Menu
Back to cohort
Record W3215225480 · doi:10.1002/9783527816392.ch10

Processing of Nonmetal Fraction from Printed Circuit Boards and Reutilization

2021· other· en· W3215225480 on OpenAlexaff
Amit Kumar, Maria Holuszko

Bibliographic record

Venuenot available
Typeother
Languageen
FieldEnvironmental Science
TopicRecycling and Waste Management Techniques
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsNonmetalPrinted circuit boardFraction (chemistry)Mass fractionElectronic wasteElectronic equipmentWaste managementNon-negative matrix factorizationMaterials scienceEnvironmental scienceMetalEngineeringComposite materialChemistryMetallurgyElectrical engineeringOrganic chemistryPhysics

Abstract

fetched live from OpenAlex

Printed circuit boards (PCBs) are the essential block of electronic equipment. With the metal concentration of 30–35% in PCB, it contains 40–80% of the total estimated value of waste PCB, whereas the rest is the nonmetal fraction such as resin, glass fibers, cellulose, and flame retardants. The separated nonmetal fraction (NMF) accounts for approximately 70% of the total weight and is usually sent to landfills. The materials present in the NMF could provide economic benefits to the recyclers and reduce the environmental concerns associated with improper disposal. Some researchers have suggested alternative use of NMF as secondary materials in various applications, and other researchers have shown the possibility of the physical and chemical recycling of NMF. This chapter provides the details of the nonmetal fraction of the waste printed circuit boards, its composition, and benefits associated with NMF recycling. It also provides an overview of physical and chemical recycling processes for NMF and potential end usage of NMF after metal recovery.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.823
Threshold uncertainty score0.992

Codex and Gemma teacher scores by category

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)0.0090.000

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 head0.016
GPT teacher head0.248
Teacher spread0.232 · 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 teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreOther

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
Published2021
Admission routes1
Has abstractyes

Explore more

Same topicRecycling and Waste Management TechniquesFrench-language works237,207