Mechanical Properties of Three-Dimensional Microstructures Infiltrated by Carbon Nanotube/Epoxy Nanocomposite Under Shear Flow
Bibliographic record
Abstract
Three-dimensional interconnected microfluidic channels fabricated by the direct-write method were infiltrated with SWCNT/epoxy nanocomposites under high shear flow to mechanically characterize the effect of single-walled carbon nanotubes (SWCNTs) spatial orientation in thermosetting-matrix nanocomposites. The micron-size fugitive ink filaments were deposited layer by layer in order to form a scaffold followed by its encapsulation by an epoxy resin. Three-dimensional interconnected microfluidic channels were then obtained by heat curing the encapsulated epoxy followed by fugitive ink removal by liquefying it at high temperature under vacuum. To debundle the Laser-ablated single-walled carbon nanotubes (La-SWNTs), nitric acid treatment following introduction of protoporphyrin IX as surfactant were done to prevent reclustering of the CNTs after separation. La-SWNTs were then mixed with ultraviolet-curable epoxy using a three-roll mill machine to achieve a well-dispersed nanocomposite. The nanocomposites were then infiltrated within the empty channels at high pressures to induce shear. High shear flow infiltration of nanocomposites will cause the CNTs to be aligned in the direction of the channels where an increase in shear leads to an increase in CNT alignments. Finally, in order to mechanically investigate the effectiveness of the infiltration technique and the orientation of SWCNTs, tensile and three-point bending tests were done.
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".