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Record W2998941494 · doi:10.5772/intechopen.86581

Spin Transport in Nanowires Synthesized Using Anodic Nanoporous Alumina Films

2020· book-chapter· en· W2998941494 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

fundA Canadian funder is recorded on the work.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueIntechOpen eBooks · 2020
Typebook-chapter
Languageen
FieldMaterials Science
TopicAnodic Oxide Films and Nanostructures
Canadian institutionsnot available
FundersUniversity of CincinnatiIntel CorporationUniversity of AlbertaVirginia Commonwealth UniversityNational Science Foundation
KeywordsNanowireNanoporousMaterials scienceAnodeNanotechnologySpin (aerodynamics)FerromagnetismCondensed matter physicsElectrodeChemistryPhysical chemistry

Abstract

fetched live from OpenAlex

Spin transport in restricted dimensionality structures (e.g., nanowires) have unusual features not observed in bulk samples. One popular method to synthesize nanowires of different materials is to electrodeposit them selectively within nanometer diameter pores in anodic alumina films. Different materials can be sequentially deposited within the pores to form nanowire “spin valves” consisting of a spacer nanowire sandwiched between two ferromagnetic nanowires. This construct allows one to study spin transport in the spacer nanowire, with the ferromagnetic contacts acting as spin injector and detector. Some of our past work related to the study of spin transport in organic and inorganic nanowire spin valves produced using nanoporous anodic alumina films is reviewed in this chapter.

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.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.638
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.024
GPT teacher head0.249
Teacher spread0.224 · 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