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Nanowires for energy

2012· editorial· en· W2018714886 on OpenAlexaff
Ray LaPierre, Mahendra K. Sunkara

Bibliographic record

VenueNanotechnology · 2012
Typeeditorial
Languageen
FieldEnergy
TopicSolar-Powered Water Purification Methods
Canadian institutionsMcMaster University
Fundersnot available
KeywordsNanowireMaterials scienceNanotechnologyEngineering physicsEngineering

Abstract

fetched live from OpenAlex

This special issue of Nanotechnology focuses on studies illustrating the application of nanowires for energy including solar cells, efficient lighting and water splitting. Over the next three decades, nanotechnology will make significant contributions towards meeting the increased energy needs of the planet, now known as the TeraWatt challenge. Nanowires in particular are poised to contribute significantly in this development as presented in the review by Hiralal et al [1]. Nanowires exhibit light trapping properties that can act as a broadband anti-reflection coating to enhance the efficiency of solar cells. In this issue, Li et al [2] and Wang et al [3] present the optical properties of silicon nanowire and nanocone arrays. In addition to enhanced optical properties, core–shell nanowires also have the potential for efficient charge carrier collection across the nanowire diameter as presented in the contribution by Yu et al [4] for radial junction a-Si solar cells. Hybrid approaches that combine organic and inorganic materials also have potential for high efficiency photovoltaics. A Si-based hybrid solar cell is presented by Zhang et al [5] with a photoconversion efficiency of over 7%. The quintessential example of hybrid solar cells is the dye-sensitized solar cell (DSSC) where an organic absorber (dye) coats an inorganic material (typically a ZnO nanostructure). Herman et al [6] present a method of enhancing the efficiency of a DSSC by increasing the hetero-interfacial area with a unique hierarchical weeping willow ZnO structure. The increased surface area allows for higher dye loading, light harvesting, and reduced charge recombination through direct conduction along the ZnO branches. Another unique ZnO growth method is presented by Calestani et al [7] using a solution-free and catalyst-free approach by pulsed electron deposition (PED). Nanowires can also make more efficient use of electrical power. Light emitting diodes, for example, will eventually become the dominant lighting technology due to its superior electrical to optical conversion efficiency. A unique LED structure based on CdS is presented by Ye et al [8]. A detailed study by Nguyen et al [9] provides a fundamental understanding of the non-radiative recombination mechanisms in GaN-based white light emitting nanowire diodes grown on Si substrates. Another application of III-nitrides is in photovoltaic devices (solar cells) [10]. InGaN is the only semiconductor alloy whose energy bandgap can be continuously varied across nearly the entire solar spectrum, promising a new generation of solar cells. Another potentially important application for nanowires is the efficient production of H 2 from the photocatalytic splitting of water, where the H 2 can be used as an energy carrier. Water splitting based on unique nanostructures include Fe 2 O 3 [11], CuS/ZnO [12], and ZnO/Si [13]. Another candidate for photocatalysis, among other applications, is copper oxide nanowires, reviewed by Gregor et al [14]. References [1] Hiralal P, Unalan H E and Amaratunga G A J 2012 Nanotechnology 23 194002 [2] Li J, Yu H and Li Y 2012 Nanotechnology 23 194010 [3] Wang B and Leu P W 2012 Nanotechnology 23 194003 [4] Yu L, O'Donnell B, Foldyna M, and Roca i Cabarrocas P 2012 Nanotechnology 23 194011 [5] Zhang F, Song T and Sun B 2012 Nanotechnology 23 194006 [6] Herman I, Yeo J, Hong S, Lee D, Nam K H, Choi J, Hong W, Lee D, Grigoropoulos C P and Ko S H 2012 Nanotechnology 23 194005 [7] Calestani D, Pattini F, Bissoli F, Gilioli E, Villani M and Zappettini A 2012 Nanotechnology 23 194008 [8] Ye Y, Yu B, Gao Z, Mang H, Zhang H, Dai L and Qin G 2012 Nanotechnology 23 194004 [9] Nguyen H P T, Djavid M, Cui K and Mi Z 2012 Nanotechnology 23 194012 [10] Wierer J J Jr, Li Q, Koleske D D, Lee S R L and Wang G T 2012 Nanotechnology 23 194007 [11] Chernomordik B D, Russell H B, Cvelbar U, Jasinski J B, Kumar V, Deutsch T and Sunkara M K 2012 Nanotechnology 23 194009 [12] Lee M and Yong K 2012 Nanotechnology 23 194014 [13] Sun K, Madsen K, Andersen P, Bao W, Sun Z and Wang D 2012 Nanotechnology 23 194013 [14] Gregor F and Cvelbar U 2012 Nanotechnology 23 194001

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)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Editorial · Consensus signal: Editorial
Teacher disagreement score0.044
Threshold uncertainty score0.147

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0020.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0030.003
Open science0.0010.002
Research integrity0.0040.007
Insufficient payload (model declined to judge)0.0440.030

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 head0.018
GPT teacher head0.298
Teacher spread0.280 · 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 designNot applicable
Domainnot available
GenreEditorial

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".

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Citations11
Published2012
Admission routes1
Has abstractyes

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