MétaCan
Menu
Back to cohort
Record W2276611759 · doi:10.1149/ma2014-01/40/1495

Development of a Miniaturized Dissolved Oxygen Sensor with Anti-Biofouling Coating for Water Monitoring

2014· article· en· W2276611759 on OpenAlexaff
HuanHsuan Hsu, Fei Du, Qiyin Fang, Ravi Selvaganapathy, Chang‐Qing Xu

Bibliographic record

VenueECS Meeting Abstracts · 2014
Typearticle
Languageen
FieldChemical Engineering
TopicAnalytical Chemistry and Sensors
Canadian institutionsMcMaster University
Fundersnot available
KeywordsOxygen sensorPolydimethylsiloxaneMiniaturizationMaterials scienceFluorescenceBiofoulingMembranePolyethylene glycolSIGNAL (programming language)Aqueous solutionCoatingOxygenOptodeOptoelectronicsNanotechnologyChemistryOptics

Abstract

fetched live from OpenAlex

The consumption of dissolved oxygen (DO) in water is indicative of aqueous organic content and therefore contamination in water 1 . Optical DO sensors consist of an oxygen sensitive fluorescence membrane on which oxygen can cause the quenching effect; the quenched fluorescence intensity is proportional to oxygen concentration 2 . These optical DO sensors are highly sensitive and long-term stable; however, the complex optical design and optical components make the cost always more expensive than electrochemical DO sensors. In order to reduce the cost, microfabricated optical DO sensing device is developed 3 ; nevertheless, the sensitivity is sacrificed owing to the small sensing area resulting from miniaturization. Conventional optical DO sensing setup has single signal excitation site which contributes to the fact that the devices cannot provide sufficient signal intensity when being miniaturized (Fig. 1 (a)). In the light of this, we designed a multi-reflection (MR) optical DO sensor for overall sensitivity enhancement. Experimental results showed that the sensitivities can be increased by more than 3 times in MR sensors. In another aspect, biofouling is one of the major challenges of all water sensors 4 ; therefore, we grafted polyethylene glycol (PEG) on the raw materials (polydimethylsiloxane, PDMS) of DO sensitive membrane. Reduction of protein adsorption (first step of biofouling) was observed as a result. Experiments Results and Discussions The optical scheme of MR optical DO sensor is shown in Fig. 1 (b). The fluorescence DO sensitive membrane was immobilized in a reservoir with total reflective (gold) surface at its bottom. A 455 nm laser was used as light source for generating fluorescence. A fluorescence-choosing band-pass filter is placed on top of the reservoir for not only preventing the excitation light from detected by a CCD sensor but also reflecting the excitation light into the reservoir again for MR purpose. After excitation, the generated fluorescence from membrane was collected by a convex lens and read by a commercially available spectrometer. The synthesis of DO sensitive membrane is guided by the following procedure: first, the PDMS elastomer (Dow Corning) was mixed with crosslinker at 10 to 1 weight ratio. Then 1 mg of the luminescent dye (Tris(bipyridine)ruthenium(II) chloride, Sigma Aldrich) was added to the polymer solution and stirred until uniformly mixed. The mixture was spin-coated on a three-inch silicon wafer at 4000 rpm for 30 second to obtain a membrane with 20 µm thickness. The DO sensing results suggest that the sensitivity is increased three times when using MR device as compared with the conventional setup. Contact angles of PDMS surfaces before and after PEG grafting are shown in Fig. 2 (a). The results show that the contact angle of PDMS is reduced significantly after PEG grafting (from 106.4 to 24.3), which illustrates the facts that the hydrophobicity of PDMS is reduced and that the protein (hydrophobic) absorption is restricted as showed in Fig. 2 (b). Conclusion The MR DO sensing device shows three times higher sensitivity as compared with signal excitation optical DO sensing setup. The result suggests that the multi-reflection device is a good replacement of miniaturized optical DO sensor. PEG grafting results in our experiments also show good anti-protein absorption capacity which may be effective in eliminating the biofouling progress. Reference 1. U.S. Environmental Protection Agency. in Volunteer Estuary Monitoring: A Methods Manual (U.S. Environmental Protection Agency) 9–3 (Office of Water, Washington, DC, 2006). 2. McDonagh, C., Maccraith, B. D. & McEvoy, a K. Tailoring of sol-gel films for optical sensing of oxygen in gas and aqueous phase. Analytical chemistry 70, 45–50 (1998). 3. McEvoy, A., McDonagh, C. & MacCraith, B. Dissolved oxygen sensor based on fluorescence quenching of oxygen-sensitive ruthenium complexes immobilized in sol–gel-derived porous silica coatings. Analyst 121, 785–788 (1996). 4. Flemming, H.-C. Biofouling in water systems--cases, causes and countermeasures. Applied microbiology and biotechnology 59, 629–40 (2002).

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)0.000
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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.017
GPT teacher head0.237
Teacher spread0.219 · 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
Published2014
Admission routes1
Has abstractyes

Explore more

Same venueECS Meeting AbstractsSame topicAnalytical Chemistry and SensorsFrench-language works237,207