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Record W3213431515 · doi:10.1149/ma2021-02581751mtgabs

Additive Manufacturing of Microcantilevers of Varying Stiffness for Sensing Applications

2021· article· en· W3213431515 on OpenAlexaff
Shervin Foroughi, Mohsen Habibi, Muthukumaran Packirisamy

Bibliographic record

VenueECS Meeting Abstracts · 2021
Typearticle
Languageen
FieldEnvironmental Science
TopicSmart Materials for Construction
Canadian institutionsConcordia University
Fundersnot available
KeywordsStiffnessMaterials scienceComputer scienceComposite material

Abstract

fetched live from OpenAlex

Introduction Microcantilevers have numerous applications in physical, biological and chemical sensing. They have been employed for determination of the fluid viscosity and density [1], detection of biomolecular interaction [2], and part per billion (ppb) level of ammonia in ambient atmosphere [3]. The silicon-based microcantilevers are the most common type of these microdevices, however the biological and physical applications of the polymeric and polymer-composite microcantilevers have been reported in the literature [4], [5]. The polymeric cantilevers have lower Young’s modulus compared to the ones made of silicon and are more sensitive for static deflection measurement [6]. The polydimethylsiloxane (PDMS) and SU-8 are examples of polymers used for fabricating cantilevers through the soft-lithography process. Although the soft-lithography is a matured process for fabrication of the micro-electromechanical systems (MEMS), it is time consuming and costly in comparison with the additive manufacturing process. This may justify further investigations on employing Additive Manufacturing (AM) technologies in fabrication of the MEMS devices. In this study, we report additive manufacturing of the polymeric microcantilevers with different dimensions using SLA 3D printing technology for employing in micro-sensing applications. Method Four 300 μm width microcantilevers with different lengths and thicknesses, were 3D printed using the Form 2 SLA 3D printer. Table 1 presents the dimensions of the cantilevers. Figure 1 shows the images of the green parts on the building platform and the final parts after post processing. 50 μm was chosen as a thickness of each layer for printing. The building material was Flexible resin (Formlabs, USA). Table 2 presents the characteristics of the printing material. The printing process of all cantilevers took 2.5 hours. To investigate the mechanical property of printed cantilevers, the linear stiffness of each part was determined by the static load-deflection tests. The tests were performed by imposing the deflection to the tip of cantilever and recording the corresponding load. The maximum deflection applied to cantilevers was 300 μm. The FemtoTools FT-RS1002 Microrobotic Measurement System was employed to perform the measurements using the mounted sensing probe with the needle tip section size of (50 × 50 μm), force range of ±100000 μN, and resolution of 5 μN. Figure 2 illustrates the front and side views of the sensing probe in contact with the one of cantilevers at the beginning of the measurement test. Results and Conclusions The length and thickness of the microcantilever affect its sensitivity. Figure 3 displays the force-deflection graphs of microcantilevers with different length-thickness ratios obtained from the tests explained in methodology. Due to the small deflection imposed to all plastic microcantilevers with respect to their dimensions, the results show the bilinear response of the cantilevers. The linear relation between the force and the deflection can be observed when the samples deflects beyond the 80 μm. Despite various dimensions of the fabricated microcantilevers, the graph confirms that the length-thickness ratio affects the stiffness of the beams which is determined by the slope of each graphs. Table 3 presents the stiffness of fabricated samples. The graphs show increasing the length-ratio of the microbeams results in increasing the stiffness of the beam. The mechanical stiffnesses of the low length-thickness ratio microcantilevers, samples 1 and 2, are comparable with the PDMS cantilever’s stiffness reported in reference [7] . This reveals the possibility of employing the SLA 3D printing method in fabrication of the operational microcantilevers. This manufacturing method offers the simplicity, time and cost reduction in fabrication of the microcantilevers. References Kim, Deokman, et. al. "Determination of fluid density and viscosity by analyzing flexural wave propagations on the vibrating micro-cantilever." Sensors 17, no. 11 (2017): 2466. Amritsar, Jeetender, et. al. "Conformational detection of heat shock protein through bio-interactions with microstructures." Research on Biomedical Engineering 36, no. 1 (2020): 89-98. Liu, Manyi, et. al. "Revealing humidity-enhanced NH3 sensing effect by using resonant microcantilever." Sensors and Actuators B: Chemical 257 (2018): 488-495. Seena, V., et. al. "Polymer microcantilever biochemical sensors with integrated polymer composites for electrical detection." Solid State Sciences 11, no. 9 (2009): 1606-1611. Sadabadi, Hamid, and Muthukumaran Packirisamy. "Nano-integrated suspended polymeric microfluidics (SPMF) platform for ultra-sensitive bio-molecular recognition of bovine growth hormones." Scientific reports 7, no. 1 (2017): 1-10. Chaudhary, Monika, and Amita Gupta. "Microcantilever-based sensors." Defence Science Journal 59, no. 6 (2009): 634-641. Nezhad, Amir Sanati, et. al. "PDMS microcantilever-based flow sensor integration for lab-on-a-chip." IEEE Sensors journal 13, no. 2 (2012): 601-609. Figure 1

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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 score0.040
Threshold uncertainty score0.386

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 head0.011
GPT teacher head0.225
Teacher spread0.214 · 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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Published2021
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