Bibliographic record
Abstract
A novel non-contact micro- and nanomanipulation method is proposed. This method, Dipole Field Manipulation (DFM), exploits the distortion of a uniform magnetic field induced around ferromagnetic cores. While this distortion can generate strong magnetic gradients to induce pulling forces on a manipulated object, the direction of the field can be used to induce magnetic torques. This paper presents a first insight on DFM capabilities and control possibilities. We show that by varying the orientation and strength of the uniform field, DFM has the potential to achieve effective manipulation in up to six degrees of freedom. Using conventional electromagnetic coils as the uniform field source and a mobile core, gradients can reach 1-2 T/m or more in the whole manipulation workspace. Such coils provide fast directional force and torque variation capabilities, and allow an easy access to the workspace for feedback visualization and core positioning. A first concept for a 6-DOF DFM system is proposed. Results of a preliminary experiment where a particle was moved around a 0.5×1 mm rectangular path demonstrate the feasibility of the method to induce sufficient forces in any direction.
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 | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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.
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".