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Design and function of molecular and bioelectronics devices

2007· article· en· W2084514339 on OpenAlexaboutno aff
Predrag Krstić, Erica Forzani, Nongjian Tao, Anatoli Korkin

Bibliographic record

VenueNanotechnology · 2007
Typearticle
Languageen
FieldEngineering
TopicNanotechnology research and applications
Canadian institutionsnot available
Fundersnot available
KeywordsBioelectronicsMaterials scienceNanotechnologyFunction (biology)OptoelectronicsBiosensorBiology

Abstract

fetched live from OpenAlex

Further rapid progress of electronics, in particular the increase of computer power and breakthroughs in sensor technology for industrial, medical diagnostics and environmental applications, strongly depends on the scaling of electronic devices, ultimately to the size of molecules. Design of controllable molecular-scale devices may resolve the problem of energy dissipation at the nanoscale and take advantage of molecular self-assembly in the so-called bottom-up approach. This special issue of Nanotechnology is devoted to a better understanding of the function and design of molecular-scale devices that are relevant to future electronics and sensor technology. Papers contained in this special issue are selected from the symposium Nano and Giga Challenges in Electronics and Photonics: From Atoms to Materials to Devices to System Architecture (12-16 March, 2007, Phoenix, Arizona, USA), as well as from original and novel scientific contributions of invited world-renown researchers. It addresses both theoretical and experimental achievements in the fields of molecular and bioelectronics, chemical and biosensors at the molecular level, including carbon nanotubes, novel nanostructures, as well as related research areas and industrial applications. The conference series Nano and Giga Challenges in Electronics and Photonics was launched as a truly interdisciplinary forum to bridge scientists and engineers to work across boundaries in the design of future information technologies, from atoms to materials to devices to system architecture. Following the first two successful meetings in Moscow, Russia (NGCM2002) and Krakow, Poland (NGCM2004), the third Nano and Giga Forum (NGC2007) was held in 2007 hosted by Arizona State University. Besides this special issue of Nanotechnology, two other collections (in the journal Solid State Electronics and the tutorial book in the series Nanostructure Science and Technology Springer) have published additional selected and invited papers from NGC2007. The NGC2007 meeting, which included two days of tutorials (Spring School) and a three day symposium, attracted approximately 400 participants from academic, industrial and governmental research institutions from 41 countries, and covered recent developments in the fabrication and functionality of nano-scale materials, devices and system architecture from advanced CMOS to molecular electronics, photonics, optoelectronics and magnetic materials and devices. The success of the conference would not have been possible without generous support from many sponsors and research institutions, especially from Arizona State University (conference host and co-organizer), International Science and Technology Center (ISTC), National Science Foundation (NSFT), Defense Advanced Research Agency (DARPA), Office of Naval Research, Army Research Office, Computational Chemistry List (CCL), Springer Publisher, City of Tempe, STMicroelectronics, Quarles & Brady LLP, Oak Ridge National Laboratory, Canadian Consulate in Phoenix, Salt River Project (SRP) and many other local, national and international and individual supporters. We would like to acknowledge the shared responsibility for this special issue of Nanotechnology on molecular and bioelectronics, and the highly professional support from Dr Nina Couzin, Dr Alex Wotherspoon and the Nanotechnology team from the IOP Publishing. We also acknowledge the exception made in allowing the publication of some material that is outside the normal scope of Nanotechnology.

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 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 score0.397
Threshold uncertainty score0.285

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)0.0000.000

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 head0.007
GPT teacher head0.229
Teacher spread0.222 · 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.

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

Citations1
Published2007
Admission routes1
Has abstractyes

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