A MEMS Gravimeter with Buckling-Beam Nonlinear Springs for Enhanced Sensitivity and Dynamic Range
Bibliographic record
Abstract
Accurate measurement of variations in local gravitational acceleration is crucial for geophysical research, natural hazard forecasting, and resource exploration. However, existing gravimeters are expensive, heavy, large, and power-hungry, motivating research on gravimeters based on micro-electromechanical systems. This paper introduces a buckling-based nonlinear spring mechanism. Through proper structural design, the buckling of two beams results in a significant reduction of the effective stiffness of the structure and, hence, improving sensitivity under the desired load. A prototype device was designed to demonstrate the working principle. The design demonstrated a remarkable drop in resonant frequency from ~110Hz to 30Hz in simulations, corresponding to a ~13×increase in sensitivity. On the other hand, the buckling of the support beams occurs gradually over a wide range of input forces, allowing the device to simultaneously achieve a high dynamic range. The designed prototype was fabricated through standard microfabrication processes and characterized. The device utilized an on-chip optical interferometer between an optical fiber and the proof mass sidewall to monitor displacements of proofmass due to input accelerations. The optical displacement sensitivity was 0.6 mV/nm with a displacement noise floor of 40 pm/√Hz at 2 Hz, The gravimeter demonstrated a sensitivity of 23,4 V/g and a dynamic range of 276 mg(1g ≈ 9,81 m/s1).
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 | 0.001 |
| 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".