Investigation on the Electrochemical Manipulation of Bio-Molecules in Glass Nanofluidic Channels
Bibliographic record
Abstract
As the size of microfluidic channel further shrinks to nanometer, the dimension is approximate to biomolecules as well as Debye length (DL). Great deals of phenomena which do not exist in the usual world will appear. The overlapping of electrical double layers (EDL) in the channel and the increasing of the viscosity are such good examples. All of these phenomena lead to the fundamental research such as colloid science, transport process and micro/nanoscale hydrodynamics. It demands more advanced technique for micro/nanoscale design and fabrication as the channels downing to nanometer scale. In this work, molecular dynamics was adopted to calculate the transport of proteins and water molecules in nanofluidic channels. New methods of nanochannel fabrication were developed based on glass substrate. Glass nanochips were achieved via ultraviolet lithography and wet chemical etching. The channel depth could be adjusted by controlling the etching time. Finally the scanning electron microscope (SEM) and surface profiler were used to characterize the shape and surface morphology of the nanochannel in detail. This study presents the feasibility of such design and fabrication methods, which gives an interesting exploration for the application of nanofluidic technology.
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) | 0.000 | 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".