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Record W4240452909 · doi:10.1149/ma2016-02/51/3843

(Keynote) Chemical Selectivity and Micro/Nano Sensors

2016· article· en· W4240452909 on OpenAlexaff
K. Prashanthi, Arindam Phani, Thomas Thundat

Bibliographic record

VenueECS Meeting Abstracts · 2016
Typearticle
Languageen
FieldEngineering
TopicAdvanced Chemical Sensor Technologies
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsSelectivityNanotechnologyMaterials scienceNanowireSensitivity (control systems)DetectorAnalytePhotothermal therapyChemistryComputer scienceElectronic engineering

Abstract

fetched live from OpenAlex

Nano and microsensors sensors, such as cantilevers, nanowires, inter-digital electrodes, etc., offer unprecedented sensitivity in chemical detection and remain as leading candidates for applications ranging from health-care to national security. They also have all the much desired sensor characteristics such as miniature size, low power consumption, simultaneous detection of multiple analytes, easy readout, and potential low cost. In general, molecular adsorption-induced changes in physical properties of the sensor serve as the sensor signal. However, many of these sensors suffer from lack of selectivity. Imparting chemical selectivity for reversible detection of small molecules has been a challenge. Commonly used method of using immobilized chemoselective coatings often results in high false positives due to the generic nature of reversible chemical interactions. Therefore, developing concepts that can provide high selectivity without relying on immobilized chemical interfaces has been an active area of research. Although infrared spectroscopy techniques offer excellent selectivity, they do not have required sensitivity. However, concepts that can combine microfaricated sensors with infrared spectroscopy offer excellent selectivity as well as sensitivity. The sensitivity of detection in the nanomechanical spectroscopic technique depends on the thermal mass of the sensor. Therefore, lowering the thermal mass of the detector could result in superior sensitivity in chemical sensing. Since sensor platforms such as nanowires have very small thermal mass, they can be an ideal sensor for photothermal spectroscopy to achieve higher sensitivity and fast response times. However, producing bi-material effect on a nanowire to cause mechanical motion is challenging. In addition, monitoring nanomechanical motion induced by small changes in temperature of a nanostructure, for example nanoribbons and nanocantilevers, requires complex and bulky readout devices. Recently we have demonstrated a method of combining photothermal spectroscopy of physisorbed molecules on a semiconductor nanowire with its electrical resonance response for unprecedented selectivity and sensitivity. This technique, therefore, combines the selectivity of mid IR spectroscopy and sensitivity offered by electrical resonance phenomenon. Wide band gap materials such as bismuth ferrite (BiFeO 3 or BFO) have high density of surface states in their band gap, which are filled up to the Fermi level. Due to the high surface-to-volume ratio of a BFO nanowire, its electrical properties are significantly influenced by the electrical nature of the surface states. Changing the temperature of the nanowire modulates the occupation of these surface states. Since the thermal mass of a nanowire is extremely small, absorption of very small quantities of heat can result in large changes in its temperature and changes the population of the surface states. Therefore, illuminating the nanowire with pulsed light can modulate the population-depopulation of these surface states depending on excitation and relaxation of molecular vibrations of the adsorbed molecules. A slight variation in temperature induces changes in the surface state population further changing the electrical properties of the nanowire, which in turn can be detected by monitoring the electrical resonance response of the nanowire. In this technique a nanowire behaves analogous to an electrical series RLC resonant circuit with effective inductance and a non-ideal capacitance, with a resonance frequency in the MHz regime. This result in a faster response time (tens of ). Unlike cantilever-based photothermal sensors, where higher thermal mass typically result in slow , this method offers response times that are 1000 times faster. Using this combined platform of electrical resonance of a BFO nanowire and mid IR photothermal effect, we have demonstrated selective and sensitive detection of phsyosrbed molecules such as TNT, RDX, PETN, etc. The sensitivity in detection is in femto gram level. Since the molecules are physisorbed on the nanowire, they can be easily desorbed making the technique reversible. IR excitation of the physisorbed molecules increases the temperature of the nanowire due to its very low thermal mass. The presence of high density of surface states on the nanowire promotes thermally induced changes in population-depopulation of surface states. Changes in population of surface states changes the electrical resonance parameters of the nanowire and its electrical resonance frequency. The BFO nanowire/IR system utilizes IR-induced internal dissipation caused by IR absorption by the adsorbed molecules. This technique can be used for detecting minute amounts of surface adsorbed molecules on similar nanomechanical resonating platforms using dissipation as the parameter. The sensitivity can be further enhanced by optimization and can provide exciting opportunities in developing a sensitive platform with superior selectivity performance. Efforts are presently underway to develop miniature IR sources to decrease the overall footprint of the integrated system.

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)0.001
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 score0.012
Threshold uncertainty score0.603

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.205
Teacher spread0.198 · 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".

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Citations0
Published2016
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