Pith. sign in

REVIEW 2 major objections 7 minor 43 references

A comparative analysis of plasmonic and dielectric metasurface sensing platforms powered by bound states in the continuum

T0 review · 2 major / 7 minor · reviewed 2026-08-15 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read Neither gold nor silicon is the best metasurface material in every environment: the paper demonstrates a loss-dependent crossover at which silicon BIC sensors hand the lead to gold ones.

desk verdict A careful material comparison for SEIRAS in lossy solvents with a plausible crossover, but the fixed n=1.33 sweep leaves the quantitative threshold solvent-specific. read the letter →

arxiv 2506.18636 v1 pith:XSDSQUE6 submitted 2025-06-23 physics.optics

Tao Jiang , Angana Bhattacharya , Martin Barkey , Andreas Aigner , Thomas Weber , Juan Wang , Stefan A. Maier , Andreas Tittl This is my paper · ORCID
classification physics.optics
keywords BoundstatesinthecontinuumSEIRASPlasmonicmetasurfacesDielectricInfraredsensingLossyenvironmentMid-infraredspectroscopyOpticallosses
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper asks which metasurface material—gold or silicon—gives stronger molecular sensing signals in realistic infrared solvents. Existing comparisons were done in air, where silicon's high-quality resonances win. The authors show that once the solvent absorbs infrared light, the ranking flips: gold's strong surface fields survive losses, while silicon's fields leak away. There is a crossover near $k_{\mathrm{env}}\approx 2\times10^{-3}$, and in highly lossy solvents both platforms do equally poorly. The practical payoff is a solvent-aware material selection rule for surface-enhanced infrared absorbance spectroscopy (SEIRAS).

What carries the argument

The central object is the quasi-bound-state-in-the-continuum (quasi-BIC) metasurface with a dual gradient: a lateral scaling factor $S$ tunes the resonance frequency continuously across the unit cells, while an elliptical tilt angle $\theta$ breaks the in-plane symmetry that turns a dark BIC into a radiative mode, setting the radiative loss. Because resonance frequency and radiative loss can be matched between silicon and gold designs, the comparison isolates the effect of intrinsic material losses. The sensing metric is the logarithmic absorbance ratio $\mathrm{Abs}=-\log(R_{\mathrm{analyte}}/R_{\mathrm{ref}})$ evaluated at the analyte's 1730 cm$^{-1}$ vibrational band.

What would settle it

Measure absorbance for optimally tilted Si and Au BIC metasurfaces in two solvents with nearly equal $k_{\mathrm{env}}$ but clearly different real refractive index, e.g. one near $n=1.33$ and one near $n=1.48$. If the sign of $\mathrm{Abs}(\mathrm{Si})-\mathrm{Abs}(\mathrm{Au})$ at $k_{\mathrm{env}}\approx 2\times10^{-3}$ differs between the two solvents, the crossover is not set by loss alone and the roadmap must be redrawn per solvent; if it is the same, the rule holds.

Watch

Extended reading notes

Core claim

On its own terms, the paper claims that no universally superior metasurface platform exists for SEIRAS: the best material is determined by the optical loss of the solvent. Using quasi-BIC metasurfaces where radiative loss can be tuned independently via the resonator tilt angle, the authors find numerically that dielectric silicon metasurfaces give higher analyte absorbance when the environment's imaginary refractive index is below about $2\times10^{-3}$, gold plasmonic metasurfaces give higher absorbance above that, and the two converge at high losses. Experiments with PMMA-coated metasurfaces in air, D2O, DMSO, and water, delivered through a microfluidic cell, reproduce this ordering with silicon best in air and gold better in the lossy solvents.

Load-bearing premise

The numerical sweep that defines the crossover fixes the solvent's real refractive index at $n=1.33$ while varying only its imaginary part, so real solvents with different real indices could in principle shift the threshold.

Editorial extensions

If this is right

  • For dry or low-loss use, silicon BIC metasurfaces give the stronger PMMA absorbance, so they are the natural default when air or dry-gas measurements dominate.
  • For solvents like D2O, DMSO, or water, gold BIC metasurfaces outperform silicon once $k_{\mathrm{env}}$ exceeds about $2\times10^{-3}$, making gold the default for in-situ bioassays.
  • At very high solvent loss ($k_{\mathrm{env}}\approx0.1$), the two platforms converge to similarly weak signals, so material choice becomes secondary to optimizing geometry and readout.
  • Tuning the tilt angle $\theta$ shifts the crossover point, giving sensor designers an explicit knob to keep a preferred material competitive in a given solvent.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • If the same sweep is repeated with solvents of different real refractive index while keeping $k_{\mathrm{env}}$ fixed, the crossover threshold may move; DMSO with $n\approx1.48$ is the natural first test.
  • The loss-leakage mechanism should be generic: any dielectric resonator whose mode spreads through its volume will quench faster than a metal's surface-localized field, so the ranking likely extends beyond silicon to other high-index dielectrics.
  • A testable extension is to replace the reflectance-ratio absorbance with a phase-sensitive readout; the material ordering could change because phase responses depend differently on radiative and absorptive losses.
Share X Bluesky LinkedIn Reddit HN

Signed reviews

No signed human review yet.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

2 major / 7 minor

Summary. The paper compares the sensing performance of gold (plasmonic) and silicon (dielectric) quasi-BIC metasurfaces for surface-enhanced infrared absorbance spectroscopy (SEIRAS) in lossy liquid environments. Numerically, the authors fix the real refractive index of the environment at n = 1.33 and sweep the imaginary part kenv from 0 to 0.1, simulating a 5 nm PMMA analyte layer on both platforms and optimizing the tilt angle (asymmetry) per platform and environment. They report a crossover at kenv ≈ 2 × 10^-3: silicon yields higher absorbance for lower losses, gold for higher losses, and both converge to poor performance at very high loss. Experimentally, they use dual-gradient metasurfaces in a microfluidic setup with air, DMSO, D2O, and H2O, and find that the measured absorbance difference between Si and Au follows the simulated trend. The paper claims this provides a roadmap for choosing the metasurface material based on solvent losses.

Significance. If the crossover is robust, the result is practically valuable for SEIRAS optofluidics: it provides a quantitative rule for choosing between dielectric and plasmonic BIC metasurfaces as a function of solvent absorption. The dual-gradient metasurface design that compensates spectral shifts while allowing independent control of radiative loss is an elegant and useful experimental tool, and the combination of simulations with microfluidic measurements is a strength. The main limitation is that the quantitative threshold is established only at a single real refractive index (n = 1.33) and the experimental validation mixes solvents with different real indices, so the generality of the numerical roadmap is not yet fully supported.

major comments (2)
  1. [Section 2.1 and Figure 3d] The central quantitative claim—the crossover at kenv ≈ 2 × 10^-3, with Si superior below and Au superior above—is computed with the real part of the solvent refractive index held fixed at n = 1.33. The text gives no sweep over n and no argument that the crossover is invariant to n. Since the experiments include DMSO with n ≈ 1.48, and since changing n alters the mode overlap with the analyte, the radiative coupling, and the field confinement in Si versus Au, the threshold may shift with n. Without an n-dependence study (or a perturbation argument), the stated roadmap for solvent-based SEIRAS is only established for n = 1.33 and may require recalibration for solvents with different real indices.
  2. [Section 2.4 (labeled 2.3) and Figure 4d] The experimental confirmation mixes solvents with different real refractive indices: air (n = 1), DMSO (n ≈ 1.48), and D2O/H2O (n ≈ 1.33). The observed negative (Au-better) difference for DMSO could in principle be caused by its higher real index rather than by its loss kenv. To uniquely attribute the crossover to kenv, the simulations should be repeated with the actual complex refractive index dispersions of the measured solvents, or the experiments should include solvents with matched n but differing kenv. As presented, the experimental agreement is qualitative and does not independently validate the quantitative kenv threshold.
minor comments (7)
  1. [Section numbering] The section titled "2.3. Experimental comparison of sensing performance" is numbered the same as the preceding section "2.3. Numerical comparison of sensing performance for analyte coated metasurfaces"; the experimental section should be renumbered (e.g., 2.4).
  2. [Section 2.2, text near Figure 2f] The text states that the field is "completely quenched for kenv = 0.3" and later mentions "kenv = 0.3" again, but the relevant figure and the range discussed elsewhere use kenv = 0.03; this appears to be a typo and should be corrected.
  3. [Section 2.2, paragraph on asymmetry] The sentence "At kenv = 0.005 and, θ = 100, the maximum reflectance..." contains a typo: θ should be 10° (the text later refers to "θ = 20°" and "θ = 30°"), and the comma after "and" should be removed.
  4. [Section 2.1, lattice parameters] The text gives the lattice periods as "Px = 4000 μm and Py = 2400 μm" for both metasurfaces; for mid-infrared resonances at ~1730 cm^-1 and a 2 × 2 mm^2 field of view, these values seem to be in nanometers or otherwise misstated, and should be corrected.
  5. [Figures 2g and 3d] The field-enhancement crossover in Figure 2g is reported at kenv > 1.2 × 10^-2, while the absorbance crossover in Figure 3d is at kenv ≈ 2 × 10^-3; the paper should clarify that these are different metrics (mean electric field versus absorbance) to avoid confusion about the crossover location.
  6. [Section 2.4, Figure 4c] The choice to average the 50 highest measured Abs values is not justified; a brief rationale or a sensitivity test showing that the result does not depend on this threshold would strengthen the analysis.
  7. [References] Several references are duplicated: reference 24 is the same as reference 17, reference 25 is the same as reference 31, and reference 35 is the same as reference 37; these should be merged or cross-referenced consistently.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity; the central crossover is a forward-simulation result benchmarked by independent experiments.

full rationale

The paper's central claim—that dielectric Si metasurfaces outperform plasmonic Au metasurfaces at low solvent loss and Au outperforms Si at moderate loss, with a crossover near kenv ≈ 2e-3—is obtained from direct finite-element simulations using literature optical constants for Si, Au, CaF2, H2O, D2O, and PMMA, and then compared with independent experimental measurements in air, DMSO, D2O, and H2O. The sensing metric Abs = -log(Ranalyte/RRef) is computed from simulated and measured reflectance spectra before and after coating with a 5 nm PMMA layer; no parameter is fitted to the experimental outcome, and no equation defining the crossover is inserted by hand. The use of dual-gradient BIC metasurfaces from the authors' prior work is a design choice, not a load-bearing premise whose truth would force the material ranking; the comparison would still be meaningful if those cited designs were replaced by any other method of tuning resonance frequency and radiative loss. The fixed real refractive index n = 1.33 in the numerical sweep while experiments include DMSO with n ≈ 1.48 is a possible correctness or generalizability limitation, but it is not circular: the simulation is not defined in terms of the experimental result, and the experimental data serve as an external benchmark. No self-definitional, fitted-input-as-prediction, self-citation-load-bearing, imported-uniqueness, ansatz-smuggling, or renaming pattern is present. The paper is self-contained against external benchmarks and its central comparison is independent of its own prior conclusions.

Assumptions & free parameters 3 free parameters · 4 assumptions · 0 invented entities

The paper rests on standard Maxwell simulations and design optimization rather than new physical entities. The main ledger items are the per-environment optimal asymmetry angle and the simplifying choice of fixed n = 1.33. No new particles, forces, or conserved quantities are introduced.

free parameters (3)
  • Optimal asymmetry angle theta_opt for each platform and environment = Si: 17.2 degrees, Au: 11.4 degrees at kenv = 0.005; varies with kenv (Figure 3c)
    The comparison uses the tilting angle that maximizes absorbance in each environment so that radiative losses are 'excluded'. Since theta_opt differs by material and kenv, the claimed crossover is conditional on this per-environment optimization.
  • Analyte layer thickness = 5 nm in numerical model; 0.20% PMMA spin-coating in experiments, thickness not directly reported
    The sensing metric Abs depends on the assumed uniform 5 nm PMMA layer in simulations. The experimental thickness is inferred from spin-coating parameters rather than measured, and no thickness comparison is made between Au and Si surfaces.
  • Data selection threshold of 50 highest Abs values = 50 per solvent
    Mean experimental absorbance is computed from the 50 highest gradient-pixel values per solvent. This selection rule can bias the comparison toward optimal regions and is not independently justified.
assumptions (4)
  • domain assumption CST finite-element simulations with periodic boundary conditions accurately model the fabricated BIC metasurfaces and their reflectance.
    All numerical claims rely on this solver. No convergence study or validation spectra against measured bare metasurfaces are shown.
  • domain assumption The tilting angle theta controls radiative loss independently for silicon and gold, so optimizing theta decouples radiative loss from material loss.
    This is the basis for attributing remaining differences to material losses. The large height difference, 750 nm for Si versus 100 nm for Au, is not treated as an independent variable.
  • ad hoc to paper The real part of the solvent refractive index can be fixed at n = 1.33 while sweeping kenv without changing the qualitative crossover.
    Section 2.1 states n is held fixed 'for ease of numerical analysis'. Experimental solvents have different real refractive indices, and invariance of the ranking to n is not demonstrated.
  • domain assumption Abs = -log(Ranalyte / RRef) at the PMMA peak is a faithful sensitivity metric for comparing the platforms.
    The paper adopts this standard SEIRAS metric. It conflates resonance amplitude changes with molecular absorption contrast, but this is the quantity optimized throughout.

how reviews work

0 comments
Cite this review

Pith. "Pith review of A comparative analysis of plasmonic and dielectric metasurface sensing platforms powered by bound states in the continuum." pith.science (2026). https://pith.science/paper/XSDSQUE6

@misc{pith2026250618636,
  author       = {Pith},
  title        = {Pith review of: A comparative analysis of plasmonic and dielectric metasurface sensing platforms powered by bound states in the continuum},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/XSDSQUE6}},
  note         = {Machine review of arXiv:2506.18636}
}
read the original abstract

Nanophotonic platforms based on surface-enhanced infrared absorbance spectroscopy (SEIRAS) have emerged as an effective tool for molecular detection. Sensitive nanophotonic sensors with robust resonant modes and amplified electromagnetic near fields are essential for spectroscopy, especially in lossy environments. Metasurfaces driven by bound state in the continuum (BICs) have unlocked a powerful platform for molecular detection due to their exceptional spectral selectivity. While plasmonic BIC metasurfaces are preferred for molecular spectroscopy due to their high surface fields, enhancing the interaction with analytes, dielectric BICs have become popular due to their high-quality factors and, thus high sensitivity. However, their sensing performance has largely been demonstrated in air, neglecting the intrinsic infrared (IR) losses found in common solvents. This study evaluates the suitability of plasmonic versus dielectric platforms for in-situ molecular spectroscopy. Here, the sensing performance of plasmonic (gold) and dielectric (silicon) metasurfaces is assessed across liquid environments with varying losses resembling typical solvents. The results show that dielectric metasurfaces excel in dry conditions, while plasmonic BIC metasurfaces outperform them in lossy solvents, with a distinct crossover point where both show similar performance. Our results provide a framework for selecting the optimal metasurface material platform for SEIRAS studies based on environmental conditions.

Figures

Figures reproduced from arXiv: 2506.18636 by the authors.

Figure 1
Figure 1. Comparison of Si and Au metasurfaces for sensing performance. (a) Schematic of the silicon (Si) and gold (Au) metasurfaces, incorporating a spectral gradient (scaling factor, 𝑆) along the horizontal axis and a coupling gradient (tilting angle, θ) along the vertical axis. (b) [PITH_FULL_IMAGE:figures/full_fig_p005_1.png] view at source ↗
Figure 2
Figure 2. Metasurface response to environmental losses without analyte layer. (a) Schematic of the plasmonic (Au) metasurface and dielectric metasurface (Si) in a lossy environment of refractive index n + i 𝑘𝑒𝑛𝑣. (b) Numerically simulated reflectance of the Si (purple) and Au (yellow) metasurfaces for a constant n = 1.33 and kenv = 0 (corresponds to a loss-less environment), kenv = 0.005 (corresponds closely to the value of h… view at source ↗

Review history (2 revisions) →

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

43 extracted references · 42 canonical work pages

  1. [1]

    Altug, H., Oh, S.-H., Maier, S. A. & Homola, J. Advances and applications of nanophotonic biosensors. Nat Nanotechnol 17, 5–16 (2022)

    work page 2022

  2. [2]

    & Garoli, D

    Giovannini, G., Haick, H. & Garoli, D. Detecting COVID-19 from breath: a game changer for a big challenge. ACS Sens 6, 1408–1417 (2021)

    work page 2021

  3. [3]

    Wang, J., Maier, S. A. & Tittl, A. Trends in nanophotonics‐enabled optofluidic biosensors. Adv Opt Mater 10, 2102366 (2022)

    work page 2022

  4. [4]

    Liu, Y.-C., Ansaryan, S., Li, X., Arvelo, E. R. & Altug, H. Real-time monitoring of single-cell secretion with a high-throughput nanoplasmonic microarray. Biosens Bioelectron 202, 113955 (2022)

    work page 2022

  5. [5]

    Li, D., Zhou, H., Hui, X., He, X. & Mu, X. Plasmonic biosensor augmented by a genetic algorithm for ultra-rapid, label-free, and multi-functional detection of COVID-19. Anal Chem 93, 9437–9444 (2021)

    work page 2021

  6. [6]

    Wang, J., Pinkse, P. W. H., Segerink, L. I. & Eijkel, J. C. T. Bottom-up assembled photonic crystals for structure-enabled label-free sensing. ACS Nano 15, 9299–9327 (2021)

    work page 2021

  7. [7]

    Leong, S. X. et al. Noninvasive and point-of-care surface-enhanced Raman scattering (SERS)-based breathalyzer for mass screening of coronavirus disease 2019 (COVID-19) under 5 min. ACS Nano 16, 2629–2639 (2022)

    work page 2022

  8. [8]

    & Altug, H

    John-Herpin, A., Tittl, A. & Altug, H. Quantifying the limits of detection of surface-enhanced infrared spectroscopy with grating order-coupled nanogap antennas. ACS Photonics 5, 4117–4124 (2018)

    work page 2018

Show all 43 references
  1. [9]

    & Tian, Z.-Q

    Wang, H.-L., You, E.-M., Panneerselvam, R., Ding, S.-Y. & Tian, Z.-Q. Advances of surface-enhanced Raman and IR spectroscopies: from nano/microstructures to macro-optical design. Light Sci Appl 10, 161 (2021)

    2021

  2. [10]

    El‐Helou, A. J. et al. Optical Metasurfaces for the Next‐Generation Biosensing and Bioimaging. Laser Photon Rev 2401715 (2025)

    2025

  3. [11]

    R., Henry, A.-I., Ringe, E

    Sharma, B., Frontiera, R. R., Henry, A.-I., Ringe, E. & Van Duyne, R. P. SERS: Materials, applications, and the future. Materials today 15, 16–25 (2012)

    2012

  4. [12]

    & Altug, H

    Adato, R., Aksu, S. & Altug, H. Engineering mid-infrared nanoantennas for surface enhanced infrared absorption spectroscopy. Materials today 18, 436–446 (2015)

    2015

  5. [13]

    Li, D., Wu, X., Chen, Z., Liu, T. & Mu, X. Surface-enhanced spectroscopy technology based on metamaterials. Microsyst Nanoeng 11, 60 (2025). 17

    2025

  6. [14]

    Zhou, H., Li, D., Hui, X. & Mu, X. Infrared metamaterial for surface-enhanced infrared absorption spectroscopy: pushing the frontier of ultrasensitive on-chip sensing. Int J Optomechatronics 15, 97–119 (2021)

    2021

  7. [15]

    Huck, C. et al. Plasmonic enhancement of infrared vibrational signals: nanoslits versus nanorods. ACS Photonics 2, 1489–1497 (2015)

    2015

  8. [16]

    Ortega, M. A. et al. In situ LSPR sensing of secreted insulin in organ-on-chip. Biosensors (Basel) 11, 138 (2021)

    2021

  9. [18]

    Yoo, D. et al. High-contrast infrared absorption spectroscopy via mass-produced coaxial zero-mode resonators with sub-10 nm gaps. Nano Lett 18, 1930–1936 (2018)

    2018

  10. [19]

    & Liu, P

    Miao, X., Yan, L., Wu, Y. & Liu, P. Q. High-sensitivity nanophotonic sensors with passive trapping of analyte molecules in hot spots. Light Sci Appl 10, 5 (2021)

    2021

  11. [20]

    Hui, X. et al. Infrared plasmonic biosensor with tetrahedral DNA nanostructure as carriers for label‐free and ultrasensitive detection of miR‐155. Advanced Science 8, 2100583 (2021)

    2021

  12. [21]

    Zhou, H. et al. Metal–organic framework‐surface‐enhanced infrared absorption platform enables simultaneous on‐chip sensing of greenhouse gases. Advanced Science 7, 2001173 (2020)

    2020

  13. [22]

    Anker, J. N. et al. Biosensing with plasmonic nanosensors. Nat Mater 7, 442– 453 (2008)

    2008

  14. [23]

    & Altug, H

    Chen, K., Adato, R. & Altug, H. Dual-band perfect absorber for multispectral plasmon-enhanced infrared spectroscopy. ACS Nano 6, 7998–8006 (2012)

    2012

  15. [24]

    Rodrigo, D. et al. Resolving molecule-specific information in dynamic lipid membrane processes with multi-resonant infrared metasurfaces. Nat Commun 9, 2160 (2018)

    2018

  16. [26]

    Wang, J., Weber, T., Aigner, A., Maier, S. A. & Tittl, A. Mirror‐Coupled Plasmonic Bound States in the Continuum for Tunable Perfect Absorption. Laser Photon Rev 17, 2300294 (2023)

    2023

  17. [27]

    I., Heck, S

    Giannini, V., Fernández-Domínguez, A. I., Heck, S. C. & Maier, S. A. Plasmonic nanoantennas: fundamentals and their use in controlling the radiative properties of nanoemitters. Chem Rev 111, 3888–3912 (2011)

    2011

  18. [28]

    & Ren, B

    Yang, K., Yao, X., Liu, B. & Ren, B. Metallic plasmonic array structures: principles, fabrications, properties, and applications. Advanced Materials 33, 2007988 (2021)

    2021

  19. [29]

    Tittl, A., John‐Herpin, A., Leitis, A., Arvelo, E. R. & Altug, H. Metasurface‐ based molecular biosensing aided by artificial intelligence. Angewandte Chemie International Edition 58, 14810–14822 (2019)

    2019

  20. [30]

    Wang, T., Dong, Z., Koay, E. H. H. & Yang, J. K. W. Surface-enhanced infrared absorption spectroscopy using charge transfer plasmons. ACS Photonics 6, 1272–1278 (2019)

    2019

  21. [31]

    Aigner, A. et al. Plasmonic bound states in the continuum to tailor light-matter coupling. Sci Adv 8, eadd4816 (2022)

    2022

  22. [32]

    Barkey, M. et al. Pixelated high-Q metasurfaces for in situ biospectroscopy and artificial intelligence-enabled classification of lipid membrane photoswitching dynamics. ACS Nano 18, 11644–11654 (2024). 18

    2024

  23. [33]

    & Quidant, R

    Yavas, O., Svedendahl, M. & Quidant, R. Unravelling the role of electric and magnetic dipoles in biosensing with Si nanoresonators. ACS Nano 13, 4582– 4588 (2019)

    2019

  24. [34]

    Yesilkoy, F. et al. Ultrasensitive hyperspectral imaging and biodetection enabled by dielectric metasurfaces. Nat Photonics 13, 390–396 (2019)

    2019

  25. [37]

    Tittl, A. et al. Imaging-based molecular barcoding with pixelated dielectric metasurfaces. Science (1979) 360, 1105–1109 (2018)

    1979

  26. [38]

    & Cai, H

    Chung, T., Wang, H. & Cai, H. Dielectric metasurfaces for next-generation optical biosensing: A comparison with plasmonic sensing. Nanotechnology 34, 402001 (2023)

    2023

  27. [39]

    Melik-Gaykazyan, E. et al. From Fano to quasi-BIC resonances in individual dielectric nanoantennas. Nano Lett 21, 1765–1771 (2021)

    2021

  28. [40]

    & Kivshar, Y

    Koshelev, K., Lepeshov, S., Liu, M., Bogdanov, A. & Kivshar, Y. Asymmetric metasurfaces with high-Q resonances governed by bound states in the continuum. Phys Rev Lett 121, 193903 (2018)

    2018

  29. [41]

    Leitis, A. et al. Angle-multiplexed all-dielectric metasurfaces for broadband molecular fingerprint retrieval. Sci Adv 5, eaaw2871 (2019)

    2019

  30. [42]

    Aigner, A., Weber, T., Wester, A., Maier, S. A. & Tittl, A. Continuous spectral and coupling-strength encoding with dual-gradient metasurfaces. Nat Nanotechnol (2024) doi:10.1038/s41565-024-01767-2

    2024 doi

  31. [43]

    Richter, F. U. et al. Gradient High‐Q Dielectric Metasurfaces for Broadband Sensing and Control of Vibrational Light‐Matter Coupling. Advanced Materials 36, 2314279 (2024)

    2024

  32. [44]

    Kühne, J. et al. Fabrication robustness in BIC metasurfaces. Nanophotonics 10, 4305–4312 (2021)

    2021

  33. [45]

    Olmon, R. L. et al. Optical dielectric function of gold. Physical Review B— Condensed Matter and Materials Physics 86, 235147 (2012)

    2012

  34. [46]

    & Chapados, C

    Max, J.-J. & Chapados, C. Isotope effects in liquid water by infrared spectroscopy. III. H2O and D2O spectra from 6000tocm− 1. J Chem Phys 131, (2009)

    2009

  35. [47]

    & Liu, L

    Zhang, X., Qiu, J., Zhao, J., Li, X. & Liu, L. Complex refractive indices measurements of polymers in infrared bands. J Quant Spectrosc Radiat Transf 252, 107063 (2020). Supporting Information All data are available in the main text or the supplementary materials. 1 Supplement...

    2020

Pith tools

Reviewed August 15, 2026 · model on record in the stance chip above.