Impedance Characterization of Laser-Induced Graphene (LIG) at X and Ku Bands for Low-Profile and Flexible Microwave Structures
Bibliographic record
Abstract
Laser-induced graphene (LIG) is emerging as a sustainable and cost-effective alternative to conventional metallic conductors for microwave applications. However, the understanding of its electrical behavior at high frequencies remains limited. This work presents a comprehensive analysis of the surface impedance of LIG traces in the X (8–12 GHz) and Ku (12–18 GHz) frequency bands, by considering three different sets of laser parameters and correlating the measured electrical properties with the morphological features of the conductor. Results show that, for all the cases considered, the LIG can be modeled as a purely resistive sheet up to 18 GHz, and thus, the surface resistance remains close to its dc value when beam defocusing is applied. Conversely, for other manufacturing options, such as single-pass and multipass scribings, the surface resistance increases by 30%–40% due to sample defects. The extracted impedance is validated both numerically and experimentally using two representative microwave structures: an ultrawideband (UWB) monopole antenna (1–18 GHz) and two flexible$4\times 2$arrays of resonant scatterers working at 9 and 9.5 GHz. In both cases, good agreement is observed between simulation and measurement, with the monopole antenna showing less than 3-dB difference in the realized gain. In the case of the flexible arrays, instead, frequency shifts up to 150 MHz are reported as a result of thermally induced bending of the precursor during laser processing. These findings demonstrate the reliability of the extracted impedance parameters and underscore the importance of incorporating substrate deformation into numerical simulations for more accurate predictions.
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".