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Record W2785533532 · doi:10.1149/ma2018-01/42/2409

3D Printing of Electrically Conductive Hybrid Organic-Inorganic Materials

2018· article· en· W2785533532 on OpenAlexaff
Shreyas Shah, MD Nahin Islam Shiblee, Samiul Basher, Julkarnyne M. Habibur Rahman, Larry A. Nagahara, Thomas Thundat, Praveen Kumar Sekhar, Masaru Kawakami, Hidemitsu Furukawa, Ajit Khosla

Bibliographic record

VenueECS Meeting Abstracts · 2018
Typearticle
Languageen
FieldEngineering
TopicAdditive Manufacturing and 3D Printing Technologies
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsMaterials sciencePolymerComposite materialCarbon nanotubeConductive polymerNanocompositeGraphitePercolation thresholdElectrical conductorExtrusionNanotechnologyElectrical resistivity and conductivityElectrical engineering

Abstract

fetched live from OpenAlex

We present preparation, characterization, and 3D printing of electrically conductive Acrylonitrile butadiene styrene (ABS) polymer. The electrically conducting ABS was prepared by doping carbon fibers (150μm in length, acquired from Nippon Graphite Corporation), at 200 ̊C by using a thermo-plasto mill, with different weight percentages (wt%) of carbon fibers in ABS polymer matrix. Electrical conductivity of samples with following weight percentages (10, 15, 25, 50 and 60 wt% carbon fibers in ABS polymer matrix) were measured using 4-point probe method [1, 2], with a result that percolation threshold occurs at 25wt%, as shown in Fig 1. SEM analysis (Fig. 2) shows uniform dispersion of carbon fibers in ABS polymer matrix, when compared to previously reported methods [3, 4]. Electrical conductivity of 1.013S/m is observed at 50 wt %. We employed melt extrusion technique in order to fabricate cylindrical filament with a diameter of 1.75 mm. A standard Fused Deposition Modeling (FDM) type printer (Makerbot) was used to print the developed filament (Fig. 3). The developed ABS electrically conductive composite is being applied in applications, such as 3D printing of wires, circuits, sensors, resistors, heaters, robotics, MEMS and microfluidics devices. References: Khosla, A. (2011). Micropatternable multifunctional nanocomposite polymers for flexible soft MEMS applications(Doctoral dissertation, Applied Science: School of Engineering Science). http://summit.sfu.ca/item/12017 Khosla, A. (2012). Nanoparticle-doped electrically-conducting polymers for flexible nano-micro Systems. The Electrochemical Society Interface, 21(3-4), 67-70. doi: 10.1149/2.F04123-4if Gray, B. L., & Khosla, A. (2010). Microfabrication and applications of nanoparticle doped conductive polymers. Nanoelectronics: Nanowires, Molecular Electronics, and Nanodevices, 227. Khosla, A., & Gray, B. L. (2010, March). Fabrication of multiwalled carbon nanotube polydimethylsiloxne nanocomposite polymer flexible microelectrodes for microfluidics and MEMS. In Proc. SPIE (Vol. 7642, p. 76421V). doi: 10.1117/12.847292 Figure 1

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 score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

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)0.0020.001

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 head0.011
GPT teacher head0.214
Teacher spread0.203 · 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

Citations0
Published2018
Admission routes1
Has abstractyes

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