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Record W1554740552 · doi:10.5772/16499

Porous Silicon Sensors - from Single Layers to Multilayer Structures

2011· book-chapter· en· W1554740552 on OpenAlexaff
J. E. Lugo, M. Victoria Lorenzo Ocampo, R. Doti, Jocelyn Faubert

Bibliographic record

VenueInTech eBooks · 2011
Typebook-chapter
Languageen
FieldMaterials Science
TopicSilicon Nanostructures and Photoluminescence
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsPorous siliconMaterials sciencePorositySiliconOptoelectronicsNanotechnologyComposite material

Abstract

fetched live from OpenAlex

Introduction Origin and discoverySilicon, one of the more common elements in nature, is defined as a metalloid, which corresponds to the number 14 in the Mendeleyev Periodic Table .It is heavier than Carbon (element number 6 in the periodic table and a key component in biochemistry), but both have chemical characteristics that are very close.Since human civilization began, volcanic stones containing this metalloid in the form of dioxide were used to create the first tools and weapons.Roman historian Plinio the Elder (23AD-79AD) mentioned the Silex-Silicis (silicon stones) in one of his works as very hard stones.These roman words are the Latin origin of the name Silicon (Tomkeieff, 1941).J.J.Berzelius was credited for the discovery of this element in 1824 in Stockholm, Sweden, but Gay-Lussac and Thenard had already prepared impure amorphous silicon by 1811.After World War II, once the applied mechanical technology was ready to produce very pure silicon wafers (under the form of monolithic crystals) and succeeded to manage the problem of the surface impurities and contamination (Hull, 1999), the electronic industry jumped from the Germanium diodes to the Silicon integrated circuits and metal-oxidesemiconductor (MOS) microprocessors that helped man reach the Moon.In summary, it is safe to say that Silicon's role along our evolution extends from prehistoric times to the exploration of the Solar System.In 1956 at the U.S. Bell Laboratories, Arthur Uhlir Jr. and Ingeborg Uhlir while trying a new technique for polishing Silicon crystalline wafers observed for the first time a red-green film formed on the wafer surface (Kilian et al, 2009).Since the discovery of its luminescence properties by Leigh Canham in 1990 (Kilian et al, 2009), researchers started to study the nonlinear optical, electric and mechanical properties of this nanostructure.This effort has permitted the fabrication of uniform porous layers with diameters as small as one nanometer, and permitting an enormous inner surface density. How to prepare porous silicon (p-Si)Several techniques exist to form this structure from a pure Silicon crystalline wafer.The most popular are: electrochemical etching, stain etching and photochemical etching.Here we introduce two versions of the etching process (Anglin, n.d).

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)0.001
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 score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.002
Open science0.0010.001
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 head0.030
GPT teacher head0.239
Teacher spread0.209 · 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
Published2011
Admission routes1
Has abstractyes

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