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Record W6893501730 · doi:10.5281/zenodo.2577185

Nanosensors Market Outlook, Key Players and Demand by 2023

2019· article· en· W6893501730 on OpenAlexaboutno aff

Bibliographic record

VenueZenodo (CERN European Organization for Nuclear Research) · 2019
Typearticle
Languageen
FieldEngineering
TopicBiosensors and Analytical Detection
Canadian institutionsnot available
Fundersnot available
KeywordsNanosensorElectronicsKey (lock)On demandScale (ratio)Reliability (semiconductor)Nanoscopic scaleNanometre

Abstract

fetched live from OpenAlex

The rising application of nanosensors in electronics access control system equipment, growing demand of nanosensors from healthcare electronics industry and Internet-of-Things (IoT) are some of the recent trends observed in the global nanosensors market.\n\n\nRequest for a free sample copy of this research report @ https://www.psmarketresearch.com/market-analysis/nanosensors-market/report-sample\n\n\nNanosensors are tiny chemical, biological and surgical sensory devices having ability to sense and detect nanoparticals. Nanosensors market is gaining attention due to their ability to convey information about nanoparticles. Nanosensors are generally of the size between one nanometer and 100 nanometers. Light absorption properties of nanoscale materials are also different from the macroscale materials. Some nanoscale materials become translucent for particles smaller in size.\n\n\nIn the field of nanotechnology, nanosensors are instruments capable of monitoring physical and chemical properties of the regions which are difficult to reach and explore. Nanosensors are used in the detection of chemical, physical and other properties of simple organisms to complex cell organisms. Nanosensors can also be used for the detection of physio-mechanical properties and electro-magnetic properties. The large scope of nanosensors makes them suitable for various research and developments.\n\n\nNanosensors find their application in several sectors such as petroleum, forensic, environmental protection, fiber optic nano-cameras, labs-on-chips, cancer detection, robotics, total blood testing, mass and pressure measurement, nanoelectronics, security, military and surveillance, food and beverage, aerospace, automotive, petroleum and others.\n\n\nThe associated benefits with nanosensors, such as compact size, low power consumption, increased reliability and the ability to correspond easily with semiconductor chips, are the key drivers for the growth of nanonsensors market. The increasing nanosensors market has also led to the growth in number of supplier industries, such as design services, design software, fabrication facilities and fabrication equipment. This has eased the sourcing of raw materials for nanosensors industry.\n\n\nBased on type, nanosensors market can be divided into gas, liquid and bio-molecular nanosensors. And based on technology, nanosensors market can be segmented into hybridization technique, carbon nanotubes, sequencing, solar photovoltaic, nanowires, combined heat and powers, transcription mediated amplification (TMA), wind turbines, polymerase chain reaction (PCR), fuel cells, micro turbines, reciprocating engines and others.\n\n\nExplore report at: https://www.psmarketresearch.com/market-analysis/nanosensors-market\n\n\nSome of the major players in nanosensors market include Nanomix Inc,, Nano Detection Technologies Inc., Affymetrix Inc., attocube systems AG, Altair Nanotechnologies Inc., Bayer AG, BioCrystal Ltd., Debiotech S.A., Diabetech, Thermo Fischer Scientific, Nippon Hosp Kyokai, Specialized Imaging, Spectra Fluidics, Micro-Tech Scientific Incorporated, and Pacific Nano Technologies.\n\n\nAbout P&S Intelligence\n\n\nP&S Intelligence, is a provider of market research and consulting services catering to the market information needs of burgeoning industries across the world. Providing the plinth of market intelligence, P&S as an enterprising research and consulting company, believes in providing thorough landscape analyses on the ever-changing market scenario, to empower companies to make informed decisions and base their business strategies with astuteness.\n\n\nContact: \n\n\nP&S Intelligence\n\n\nToll-free: +1-888-778-7886 (USA/Canada)\n\n\nInternational: +1-347-960-6455\n\n\nEmail: enquiry@psmarketresearch.com\n\n\nWeb: https://www.psmarketresearch.com\n\n\nConnect with us: LinkedIn | Twitter | Google + | Facebook

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 categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.345
Threshold uncertainty score0.996

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)0.0080.005

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 head0.009
GPT teacher head0.185
Teacher spread0.176 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
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

Citations0
Published2019
Admission routes1
Has abstractyes

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