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Record W2275837360 · doi:10.1149/ma2015-02/45/1796

Functional PDMS Composite Microbridges for Temperature Sensing Applications

2015· article· en· W2275837360 on OpenAlexaff
Manu Pallapa, Jacob C. K. Leung, Pouya Rezai

Bibliographic record

VenueECS Meeting Abstracts · 2015
Typearticle
Languageen
FieldChemical Engineering
TopicAnalytical Chemistry and Sensors
Canadian institutionsYork University
Fundersnot available
KeywordsMaterials sciencePolydimethylsiloxaneMicrofabricationPhotolithographyFabricationReactive-ion etchingEtching (microfabrication)OptoelectronicsLaser ablationComposite numberComposite materialMicrofluidicsNanotechnologyLaserOptics

Abstract

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Microstructures provide excellent sensing and actuating capabilities due to their high surface to volume ratio and have brought about important advancements in life sciences research [1-3]. The development of electrically-conductive polymer composites for such sensors and actuators as well as rapid fabrication techniques will result in microstructures with better transduction, low cost of production and flexibility [4, 5]. Conventional microfabrication techniques for such transducers are photolithography, reactive ion etching (RIE), laser ablation, and focussed ion beam etching. However these techniques are limited by the requirement of multiple processing steps (photolithography and RIE) or serial processing with specialized equipment (laser ablation and focussed ion-beam etching). In this work we report a low-cost, rapid and convenient technique to microfabricate electrically-conductive Iron-Polydimethylsiloxane (Fe-PDMS) microbridges using agar as the sacrificial material and further demonstrate the temperature sensing property of this polymer composite. Fabrication of the Fe-PDMS microbridges by the sacrificial agar technique is illustrated schematically in Fig. 1. A rectangular mold containing a 20mm×0.7mm×0.2mm channel and three pairs of sidewall through-holes (800µm in diameter) was manufactured via 3D printing (Fig.1a-i). Glass capillary guide rods with diameters of 65µm, 240µm and 350µm were inserted into the sidewall though-holes and passed through the channel (Fig.1a-ii). The guide rods functioned as master molds for the sacrificial agar, creating cavities for the Fe-PDMS composite to flow through. A 4% agar solution was prepared and poured into the rectangular mold (Fig.1a-iii). Following the room-temperature curing of the agar, the guide rods were removed horizontally and the agar replica was carefully de-molded, exposing the cylindrical cavities to be filled with Fe-PDMS composite (Fig.1a-iv). The agar replica was transferred into a petri dish (Fig.1a-v). The Fe-PDMS composite was prepared by mixing 80wt% iron particles (200 mesh size) with Sylgard 184 pre-polymer (10:1 elastomer-curing agent ratio). This composite was carefully casted into the aforementioned cylindrical cavities using assistive capillary flow. Undoped Sylgard 184 pre-polymer (10:1 elastomer-curing ratio) was then casted on the entire structure and cured at 37oC for 24 hours (Fig. 1a-vi). The cured structure was then immersed in a 100˚C water bath (Fig. 1a-vii) to dissolve the sacrificial agar and dried to form the suspended microbridge structures (Fig. 1a-viii) before plasma bonding to another flat PDMS layer (Fig. 1a-ix). The scanning electron microscope images of the fabricated microbridges are shown in Fig. 2. The thickness of the fabricated suspended bridges were measured and compared against their respective guide rod thicknesses (Fig. 3). The average thicknesses of the 65µm and 240µm microbridges showed a high precision in fabrication with a standard deviation of ~12µm from the guide rods. The larger deviation of the 350µm microbridge may be attributed to the size range (1-75 µm) of the iron particles in the Fe-PDMS composite which is currently under investigation. Uniform particle size would ensure better consistency in microbridge thicknesses. The temperature sensing ability of the Fe-PDMS composite was experimentally verified as well. A 24 AWG copper wire was used to provide electrical interconnection with the composite. The current-voltage (IV) characteristics of the Fe-PDMS composite in the input voltage range of 1-20V was measured by a Keithley 2410 source meter at four equilibrium temperatures of 45, 50, 60 and 70˚C applied externally via a hot plate. Each equilibrium temperature level produced a distinctive near-ohmic IV curve (Fig. 4a) with a positive correlation between the temperature and electrical conductivity. The obtained mean resistivities (Fig. 4b) imply a positive coefficient of resistance that is analogous to metals. The sacrificial agar fabrication technique in conjunction with the properties of the developed Fe-PDMS polymer composite will be suitable for development of low-cost and flexible electrodes and microstructures in microfluidic devices for thermo-electric temperature sensing and actuating applications. References F. Mei, S. P. J. Fancy, Y.-A. a Shen, J. Niu, C. Zhao, B. Presley, E. Miao, S. Lee, S. R. Mayoral, S. a Redmond, A. Etxeberria, L. Xiao, R. J. M. Franklin, A. Green, S. L. Hauser and J. R. Chan, Nat. Med., 2014, 20, 954–960. F. Liu, Y. Piao, J. S. Choi and T. S. Seo, Biosens. Bioelectron., 2013, 50, 387–392. S. Ito, T. Yasui, Y. Okamoto, N. Kaji and M. Tokeshi, Proc. 16th Int. Conf. Miniaturized Syst. Chem. Life Sci., 2012, 1234–1236. Gong X, Wen W, Polydimethylsiloxane-based conducting composites and their applications in microfluidic chip fabrication, Biomicrofluidics 2009, 3, 012007, pp1-14 Calvert P, Deepak D, Patra P, Agrawal A, Sawhney A, Conducting Polymer and Conducting Composite Strain Sensors on Textiles. Molecular Crystals and Liquid Crystals, 2008; 484 (1), pp291-302 Figure 1

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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 score0.005
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0050.002

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 head0.023
GPT teacher head0.241
Teacher spread0.218 · 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".

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Published2015
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