Advanced Photonics, Volume. 4, Issue 1, 016003(2022)

Ultrafast impulsive Raman spectroscopy across the terahertz–fingerprint region Article Video

Walker Peterson1, Julia Gala de Pablo1, Matthew Lindley1, Kotaro Hiramatsu1,2,3、*, and Keisuke Goda1,4,5,6
Author Affiliations
  • 1The University of Tokyo, School of Science, Department of Chemistry, Tokyo, Japan
  • 2The University of Tokyo, Research Center for Spectrochemistry, Tokyo, Japan
  • 3PRESTO, Japan Science and Technology Agency, Saitama, Japan
  • 4Japan Science and Technology Agency, Tokyo, Japan
  • 5University of California, Los Angeles, Department of Bioengineering, Los Angeles, California, United States
  • 6Wuhan University, Institute of Technological Sciences, Wuhan, China
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    Figures & Tables(14)
    Schematic and principle of DIVS. (a) Optical setup for DIVS. Following dispersion compensation and a pump–probe generator, cross-polarized pump and probe pulses with variable delay (τ) are longpass filtered (LPF 1) and split at a BS into CW and CCW paths within an SI. In the CW path, the probe phase is modulated with τ dependence at the sample. A polarizer eliminates the pump pulse. At LPF 2 within the SI, a fraction of the probe is reflected to be detected as FT-CARS after an SPF. The rest of the probe interferes at the upper side of the BS with the LO generated in the CCW direction, generating the SE-ISRS signal. An iris is used to select a high-SNR region of the signal. (b) Principles of DIVS signals. At the sample, the pump pulse causes the refractive index to oscillate in time (upper panel). The τ-dependent phase modulation of a following probe results in either a spectral shift (detected in FT-CARS via filtering) or a phase delay shift (detected in SE-ISRS via interference), according to the slope of the refractive index (middle panel). Vertical lines in each spectrum indicate the optical filter cutoff separating the SE-ISRS (S) and FT-CARS (F) detection regions. The FT-CARS signal is proportional to the temporal derivative of the refractive index, while the SE-ISRS signal is proportional to the refractive index itself (lower panel). This key difference provides the complementary THz and fingerprint sensitivities of SE-ISRS and FT-CARS, respectively.
    Time-domain DIVS signals. SE-ISRS (blue, left axis) and FT-CARS (green, right axis) time-domain signals were simultaneously acquired in less than 42 μs. The zoomed-in inset corresponds temporally to the region outlined in the dashed rectangle. In the SE-ISRS signal, lower-frequency Raman modes are dominant, while higher-frequency modes are comparatively stronger in the FT-CARS signal.
    Waterfall plots of (a) SE-ISRS and (b) FT-CARS spectra obtained at 24,000 spectra/s. The sample is a 1:1 (volume ratio) mixture of bromoform and benzene. Insets show a plot of 1500 averaged spectra. Individual spectra in the waterfall plots are normalized to the average power of the noise region of 2000 to 2400 cm−1 in the corresponding 1500-spectra average plot. The lowest-frequency mode of bromoform at 154 cm−1 is detected in individual SE-ISRS spectra but not detected in individual FT-CARS spectra. The 1178-cm−1 mode of benzene is detected in individual FT-CARS spectra but not detected in individual SE-ISRS spectra. The plots highlight the complementary nature of both signals.
    Comparison of SE-ISRS and FT-CARS spectral sensitivities in DIVS. Plotted are the ratios of the average SNRs of SE-ISRS and FT-CARS signal powers of the Raman-active modes of bromoform, benzene, toluene, and tetrabromoethane as measured by DIVS (left axis). A dashed line shows equivalence between SE-ISRS and FT-CARS SNRs (left axis). The solid line indicates the theoretical ratio of SE-ISRS and FT-CARS powers in DIVS (right axis).
    Waterfall plots of (a) SE-ISRS and (b) FT-CARS spectra of bromoform. Spectra were obtained at 24,000 spectra/s. Insets show a plot of 1500 averaged spectra. Individual spectra in the waterfall plots are normalized to the average power of the noise region of 2000 to 2400 cm−1 in the corresponding 1500-spectra average plot.
    Waterfall plots of (a) SE-ISRS and (b) FT-CARS spectra of benzene. Spectra were obtained at 24,000 spectra/s. Insets show a plot of 1500 averaged spectra. Individual spectra in the waterfall plots are normalized to the average power of the noise region of 2000 to 2400 cm−1 in the corresponding 1500-spectra average plot.
    Waterfall plots of (a) SE-ISRS and (b) FT-CARS spectra of toluene. Spectra were obtained at 24,000 spectra/s. Insets show a plot of 1500 averaged spectra. Individual spectra in the waterfall plots are normalized to the average power of the noise region of 2000 to 2400 cm−1 in the corresponding 1500-spectra average plot.
    Waterfall plots of (a) SE-ISRS and (b) FT-CARS spectra of tetrabromoethane. Spectra were obtained at 24,000 spectra/s. Insets show a plot of 1500 averaged spectra. Individual spectra in the waterfall plots are normalized to the average power of the noise region of 2000 to 2400 cm−1 in the corresponding 1500-spectra average plot.
    Concentration-dependent plot of the SNR of SE-ISRS and FT-CARS signals. The FT-CARS SNR (green) is represented as the SNR of the 992-cm−1 mode of benzene, while the SE-ISRS SNR (blue) is represented as the SNR of the 222-cm−1 mode of bromoform. SNR values were calculated as the average of 15,000 spectra obtained at 24,000 spectra/s.
    Simulated frequency-dependent plot of the normalized power of SE-ISRS and FT-CARS signals in DIVS. The SE-ISRS power (blue) dominates in the low-frequency Raman spectral region, whereas the FT-CARS power (green) is higher in the fingerprint region.
    • Table 1. Mean ratios of the SNRs of SE-ISRS (SE) and FT-CARS (FT) and their standard deviations (n=10).

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      Table 1. Mean ratios of the SNRs of SE-ISRS (SE) and FT-CARS (FT) and their standard deviations (n=10).

      SampleTBEBFTBEBFTBETBETOLBENTOLTOL
      Frequency (cm1)21922353654066371478799510051211
      SNRSE/SNRFT6567775.144.331.22
      Standard deviation24.139.90.190.160.04
      SNRFT/SNRSE1.522.7217.311.316.0
      Standard deviation0.040.181.510.761.02
    • Table 2. Average SNRs and standard deviations (n=10) from individual spectra of bromoform and benzene.

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      Table 2. Average SNRs and standard deviations (n=10) from individual spectra of bromoform and benzene.

      Frequency (cm1)BromoformBenzene
      1542235406509951178
      FT-CARSAverage1.3675.80.8715820.53
      Standard deviation0.051.20.0219.40.01
      SE-ISRSAverage40.9105732992.2
      Standard deviation1.028.19.28.8
    • Table 3. Average SNRs and standard deviations (n=10) from individual spectra of toluene.

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      Table 3. Average SNRs and standard deviations (n=10) from individual spectra of toluene.

      Frequency (cm1)52478710051211
      FT-CARSAverage62.02298.85
      Standard deviation0.532.40.07
      SE-ISRSAverage3.2623.320.70.56
      Standard deviation0.171.521.40.03
    • Table 4. Average SNRs and standard deviations (n=10) from individual spectra of tetrabromoethane.

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      Table 4. Average SNRs and standard deviations (n=10) from individual spectra of tetrabromoethane.

      Frequency (cm1)6610416921945153666371410111199
      FT-CARSAverage1.6027.88.918.597.023.21
      Standard deviation0.030.530.190.140.160.09
      SE-ISRSAverage1.482.409.4010621.14144.610.95.66
      Standard deviation0.070.090.3237.80.075.130.350.20
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    Walker Peterson, Julia Gala de Pablo, Matthew Lindley, Kotaro Hiramatsu, Keisuke Goda, "Ultrafast impulsive Raman spectroscopy across the terahertz–fingerprint region," Adv. Photon. 4, 016003 (2022)

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    Paper Information

    Category: Research Articles

    Received: Sep. 21, 2021

    Accepted: Jan. 12, 2022

    Published Online: Mar. 1, 2022

    The Author Email: Hiramatsu Kotaro (hiramatsu@chem.s.u-tokyo.ac.jp)

    DOI:10.1117/1.AP.4.1.016003

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