Standing Wave Effects in O-PTIR
Bibliographic record
Abstract
Traditionally, photothermal spectroscopy (PTS) has been exploited for the detection of nanoparticles [2, 3]. With the advancement of broadband quantum cascade lasers (QCLs), optical photothermal infrared spectroscopy (O-PTIR) has gained interest for imaging applications due to its capability to combine IR specificity and submicron optical resolution [4, 5]. However, little has been reported on imaging artifacts. Based on a commercial confocal microscope, a MIR PTS instrument was developed at TU Wien to investigate photothermal image quality. Beside edge effects [1], standing wave patterns were observed in a thin-film sample. This phenomenon will be explained by investigating the effect of the detected signal on the photothermal image. [1] Aamont, L.C. and Murphy, J.C. (1982) “Effect of 3-D heat flow near edges in photothermal measurements.” Applied Optics 21(1):111-115. [2] Adhikari, S., Spaeth, P., Kar, A., Baaske, M. D., Khatua, S. and and Orrit, M. (2020) “Photothermal Microscopy: Imaging the Optical Absorption of Single Nanoparticles and Single Molecules”. ACS Nano 14:16414–16445. [3] Berciaud, S., Lasne, D., Blab, G. A., Cognet, L. and Lounis, B. (2006) “Photothermal heterodyne imaging of individual metallic nanoparticles: Theory versus experiment”. Physical Review B 73(4):045424. [4] Furstenberg, R., Crocombe, R. A., Kendziora, C. A., Papantonakis, M. R., Nguyen, V. and McGill, R. A. (2012) “Chemical imaging using infrared photothermal microspectroscopy”. Proc. of SPIE 8374.837411. [5] Zhang, D., Li, C., Zhang, C., Slipchenko, M. N., Eakins, G. and Cheng, J.-X. (2016) “Depth-resolved mid-infrared photothermal imaging of living cells and organisms with submicrometer spatial resolution”. Sci. Adv. 2(9):e1600521.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".