Open Access
Add to BibSonomy
Abstract
We demonstrate a single-photon counting Raman spectroscope and benchmark it against conventional and surface-enhanced Raman spectroscopy. For direct comparison without ambiguity, we use the same solutions of Rhodamine 6G and a common optical setup with either a spectrometer or an acousto-optic tunable filter, whereas the surface enhancement is realized with immobilized Ag nanoparticles. Our results find that the single photon counting significantly elevates the detection sensitivity by up to eight orders of magnitude, arriving at a comparable level of surface-enhanced Raman spectroscopy. Another significant advantage is with the time-resolving measurement, where we demonstrate time-gated and time-correlated single-photon counting with sub-nanosecond resolution. It offers insights into the samples’ transient responses and enables the isolation of Raman scattering from fluorescence signals.
© 2024 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement
1. Introduction
Raman’s spectroscopy is based on inelastic light scattering that is characteristic of the vibrational properties of the target molecules. As a powerful analytical technique, it is broadly employed for diverse applications. For example, it is used for molecular identification [1], characterization of chemical structures, and monitoring chemical reactions in real-time [2]. It is particularly useful for analyzing organic and inorganic compounds, as well as complex mixtures. It can also be used to study chemical processes under extreme conditions, such as high pressure and high-temperature [3], making it valuable for research in materials science and geochemistry [4]. In the field of pharmaceuticals, Raman spectroscopy is used for drug discovery [5], formulation analysis [6], and quality control [7]. It can provide information on drug-polymer interactions, drug stability, and drug release profiles from formulations, which aids in the development and optimization of drug products [8].
Despite its many advantages, Raman spectroscopy also has limitations, such as low sensitivity due to inherently weak Raman scattering and fluorescence interference [9,10]. Advancements in instrumentation, data analysis techniques, and sample preparation methods continue to address these challenges and expand the capabilities of Raman spectroscopy. One notable variation of Raman spectroscopy is surface-enhanced Raman spectroscopy (SERS) [11–14]. SERS involves the use of nanostructured metal surfaces, typically composed of nanoparticles or roughened substrates, to enhance the Raman signal of analytes adsorbed on or in close proximity to the surface [15–19]. The localized surface plasmon resonance of the metallic nanoparticles (NPs) amplifies the Raman scattering, resulting in significantly increased sensitivity and detection limits compared to conventional Raman spectroscopy [20,21].
Conventional Raman spectroscopy typically uses diffractive optics and nitrogen-cooled charge-coupled devices (CCDs) [22]. However, in recent years there has been growing interest in employing single-photon detectors (SPDs) for Raman spectroscopy due to their advantages in terms of sensitivity, speed, and quantum efficiency [23]. Among various SPD devices, avalanche photodiodes (APDs) and superconducting nanowire single-photon detectors (SNSPDs) offer exceptional performance in terms of low dark counts, high photon detection efficiency, fast response times, and excellent timing resolution [24]. Time-reseolved Raman spectroscopy with gated processing for fluorescence has been achieved by using a pulse laser instead of continuous-wave(CW) lasers [25–29]. The addition of SPDs into the system leads to Raman signal detection with high signal-to-noise ratio. [30–35].
To advance this line of study, here we present–for the first time to our best knowledge–a direct comparison of Raman signal detection among a traditional Raman spectrometer, SERS, and an SPD system equipped with a programmable acoustic optical tunable filter (AOTF). To facilitate this comparison, we designed a customized optical setup that allowed us to detect Raman signals from the same sample using each of these detection methods. We chose Rhodamine 6G (R6G) as the model analyte and prepared solutions with varying concentrations of R6G and collected the spectra of Raman shifts. Our results indicate that the SPD system exhibits superior sensitivity compared to the traditional nitrogen-cooled CCD spectrometer when using the same acquisition time. Moreover, the sensitivity of the AOTF-based SPD is on par with SERS measurements. In addition to these findings, we conducted time-resolved measurements to facilitate in-depth fluorescence analysis, encompassing integration over sub-nanosecond time intervals. Among these time windows, the one spanning wider time window displayed relatively higher fluorescence intensity, albeit with a lower peak-to-noise ratio. Furthermore, we conducted time-gated measurements using a digital delay generator with varying pulse widths, where shorter pulse widths yielded the best peak-to-noise ratio. Both these time-resolved measurements exhibit good agreement with each other.
The insights gained from this benchmarking study of photon-counting Raman spectroscopy, coupled with time-resolved analysis, have the potential to inspire researchers to extend its applications across diverse areas, including ultrasensitive Raman-based chemical and biological sensing, as well as 3D imaging [29,36].
2. Materials and methods
2.1 Experimental setup
A custom-built Raman detection setup is illustrated in Fig. 1. The 775 nm laser pulses were generated using the following setup: A mode-lock source laser (CALMAR Laser) with a pump current of 0.137 A and a repetition rate of 50 MHz [37] was utilized. Wavelength division multiplexing (WDM) was employed to select the desired wavelength of 1550.9 nm with a bandwidth of 1.18 nm. To achieve high peak power, the pulses were amplified through Erbium-Doped Fiber Amplifier (EDFA), resulting in a measured laser power of 103 mW after the amplification process. The laser beam then propagated through free space after passing through a fiber coupler. Half-wave and quarter-wave plates, along with a polarizing beam splitter, were incorporated to control the intensity and select the polarization for the nonlinear second harmonic generation (SHG) process. A telescope was used to increase the beam size to 4 mm in diameter before entering the Magnesium-doped Periodic Poled Lithium Niobate (Mg:PPLN) crystal, where the SHG process generated the approximate 775 nm wavelength. After the nonlinear crystal, two 780 nm bandpass filters (with a bandwidth of approximately $\pm$10 nm) and an 850 nm shortpass filter (with an optical density greater than 5) were positioned to allow only the transmission of the 775 nm wavelength, achieving over 100 dB of suppression for the fundamental wavelength of 1550.9 nm [36,38]. This yielded an average laser power of 21.1 mW and a peak power of 2.1 W.
Fig. 1. The sketch of the experiment setup. A mode-locked laser operating at a wavelength of 1550.9 nm serves as the pump source, generating second harmonic (SH) light at $\sim$ 775 nm. The filtered SH light is directed onto the sample (R6G). The resulting Raman output signal is detected either by a spectrometer or an acousto-optic tunable filter (AOTF)-based selective single-photon detector (SPD). The components used in the setup include an Erbium Doped Fiber Amplifier (EDFA), a free space to fiber coupler (FC), a fiber polarization controller (FPC), a half-wave plate (HWP), a polarizing beam-splitter (PBS), and a notch filter (NF).
The laser beam was then collected using a fixed focus collimator (F220FC-780, Thorlabs). The same collimator was used at the other end of the multimode fiber (62.5/125 $\mu$m). Subsequently, the laser beam passed through a narrowband filter and was reflected by a dichroic mirror towards the objective lens (100x, Nikon CFI 60 LU PLAN EPI ELWD). The beam was focused on the surface of the SERS-active silicon substrate, where the model analyte is added on. The reflected light was collected by the same objective lens and then directed through a dichroic mirror and a notch filter. Depending on the measurement setup, it was either (a) collected by a fiber coupler into a multimode fiber (with a coupling efficiency of approximately 80%) and delivered to an Acton SpectraPro 2300 spectrometer equipped with a Roper Scientific liquid nitrogen-cooled CCD camera, providing a spectral resolution of around 0.1 nm, or (b) passed through the AOTF (Brimrose Corp., Tellurium Dioxide Noncollinear crystal, TEAF10-550-1000) with a spectral resolution of approximately 1-3 nm, and then collected by the multimode fiber-coupled SPD (Excelitas, SPCM-AQRH) with a dark count of $\sim$500 Hz, dead time $\sim$25 ns and timing resolution $\sim$350 ps.
The AOTF is a high-speed electro-optical device engineered for programmable tuning of bandpass filter. It allows for the rapid and dynamic selection of specific wavelengths from broad-spectrum or multiline sources. Utilizing a first-order diffraction efficiency of roughly 80%, the AOTF was employed to selectively choose the desired wavelength from a range of Raman signals, facilitating their detection by the SPD. A customized frequency setting was employed for the AOTF scan, covering a spectrum from 96 MHz to 117 MHz. The first-order beam, having passed through the AOTF, was then coupled into a multimode fiber with a core diameter of approximately 200 $\mu$m, before being directed towards the SPD. All SERS measurements were conducted at room temperature and the acquisition time for all spectra was set at 120 seconds, unless otherwise specified. The laser’s average power at the sample was maintained at around 10 mW. The output signals from the SPD were analyzed using a Time Tagger Ultra (Swabian instrument). To control the wavelength selection by the AOTF and collect spectra from the SPD, a custom-built MATLAB code was developed and utilized. Note that the precision of time-resolved measurements is determined by both the timing jitter of the detector and the instrument response function (IRF) of the integrated time-to-digital conversion. The IRF of the time-to-digital conversion is, in turn, influenced by the timing jitter of the system’s reference clock, which is derived from the pulsed laser source. In our setup, the timing jitter of the laser pulse is less than 10 ps, a value we estimated by measuring the relative time between successive laser pulses using a time tagger [39]. This value is significantly smaller than the timing resolution of the SPD ($\sim$350 ps).
To enable time-gated and time-correlated single-photon counting, we added a 4-channel compact digital delay generator (T560, Highland Technology) into the experimental setup. It receives a pulsed signal (Radio Frequency) from the laser system as a trigger input and produces four precise pulse outputs, allowing independent programming with a resolution of 10 ps in both delay and width (with width range from 1 ns to 10 seconds). This functionality ensures that the detector remains open for a defined time interval, referred to as the gated window, following the firing of each laser pulse. One output of the digital delay generator is employed to gate the SPD and the other serves as a sync trigger for the Time Tagger Ultra. This ensures the synchronized data acquisition for subsequent data analysis.
2.2 Preparation of R6G samples and fabrication of SERS-active substrates
Figure 2 illustrates the sample preparation process. To create solution samples for detection, R6G powder obtained from Sigma-Aldrich was used. The R6G powder was diluted into various concentrations using Milli-Q water. To perform immediate tests, the R6G solution with a designated concentration was applied to a silicon wafer, as outlined below.
A $5mm\times 5mm$ fixed-size silicon wafer (TED PELLA, Inc) is selected as the substrate for all measurements. To enhance the Raman signals in SERS measurements, silver nanoparticles (AgNPs) were selected as the enhancement material. A colloidal solution of negatively charged AgNPs was prepared using a modified Lee and Meisel method, which involves the reduction of silver nitride by sodium citrate under UV radiation. In this procedure, 0.8 mL of 1% wt. sodium citrate solution was added drop by drop to a 40 mL solution of 1 mM silver nitride. The mixture was continuously stirred and placed in a UV chamber for 4 hours. To prevent an excessive temperature increase resulting from UV irradiation, a water bath was used to maintain the temperature below 50 $^{\circ }$C, ensuring the formation of mono-dispersed AgNPs. These AgNPs had an average size of 40 $\pm$ 5 nm, a $\zeta$-potential of -40 mV, and a plasmon resonance peak at 406 nm.
Fig. 2. The sample preparation procedure involved the following steps: (a) Preparation of SERS active substrates. (b) Immobilization of AgNPs on Si substrates. (c) Scanning Electron Microscope (SEM) image of AgNPs (40nm) on Si.
Since the surface of a silicon wafer with a thin native oxide also possesses a negative charge, a layer of polyallylamine hydrochloride (PAH) from Aldrich (MW 1500g) was introduced as an anchoring layer. PAH was employed to provide a positively charged substrate surface, enabling the immobilization of AgNPs through electrostatic attraction. Initially, silicon substrates were immersed in a PAH aqueous solution (0.2 mg/mL, pH 9) for 20 minutes. Subsequently, they were gently rinsed in Milli-Q water at pH 4.5. The Si substrates functionalized with PAH were then immersed in 2 mL AgNP colloidal solution ($10^{16}$ particles/mL) in darkness for 8 hours, facilitating the attachment of AgNPs to the surface. The resulting substrates were carefully rinsed in Milli-Q water at pH 4.5 and subsequently employed for SERS measurements. Similar method was conducted to produce AgNPs of size $90 \pm$ 5 nm. The silicon treated with PAH was immersed in the colloidal solution ($10^{15}$ particles/mL) for comparison to be conducted. The 40 nm and 90 nm AgNPs silicon substrate were examined by the scanning electron microscopy for topography and dispersion.
2.3 Sensitivity and enhancement factor
For comparison purposes of detection sensitivity, we use the parameter sensitivity factor (SF), similar to the enhancement factor (EF) used in the SERS measurement [14], to visualize the level of sensitivity between detectors. The EF used for the SERS Raman signal is calculated as follows:
where $I_S$ and $I_N$ are the integrated intensities of a characteristic band from SERS and normal Raman, and $C_S$ and $C_N$ are their respective concentrations of model analyte. The SF is calculated for the data from a SPD using the modified equation: where $I_{sp}$ is the integrated intensities of a characteristic band from a SPD and $C_{sp}$ is the concentration of the R6G analyte. To ensure the comparability between $I_{sp}$ and $I_N$, the normalized intensities are used for analysis. Note that the EF of SERS measurement largely depends on the SERS substrate, and many factors would have to be taken into consideration. We have previously reported an EF of $10^7$ for isolated silver or gold nanoparticles and the study of the impact of sodium chloride on EF [16].2.4 Average signal-to-noise ratio for time resolved single photon counting
The calculated average signal-to-noise ratio ($SNR_{ave}$) is determined by the equation below.
where $S_i$ is the photon count at the Raman peak and $S_B$ is the background photon count where the Raman signal is not present.3. Results
3.1 Comparison of spectrometer vs AOTF SPD measurements
Figure 3 illustrates the comparison between measurements obtained using a spectrometer and measurements conducted with an AOTF-based single photon detection. The measurements were performed using three separately prepared 20 $\mu$L droplets of the same R6G concentration to ensure accuracy and repeatability across the detection methods. For the higher concentrations of $10^{-2}$ $M$ and $10^{-3}$ $M$, data were collected using a spectrometer and an SPD without using SERS (Fig. 3 (a) and (c)). Starting from a concentration of $10^{-5}$ $M$, SERS measurements were conducted using a prepared substrate. SERS measurements were carried out up to a concentration of $10^{-8}$ $M$, as the spectral features of lower concentrations became indiscernible. Measurements using SPD alone were performed without the SERS substrate, and tentative measurements were taken up to a concentration of $10^{-10}$. All R6G droplets had a volume of 20 $\mu$L, and the measurements were conducted immediately after preparation, with an acquisition time of 120s. The same optical setup was utilized throughout the experiment, with a flip mirror serving as the switch between the spectrometer and the AOTF-based single photon detection.
To ensure concentration accuracy, one 20 $\mu$L droplet of R6G was added onto the microscope slide right before each measurement. For SERS measurements, the same droplet was added onto the SERS substrate prior to measurement. The R6G sample was used at a concentration of $10^{-3}$ $M$ and the optical signal was captured at different frequencies selected by an AOTF. The spectra obtained from the spectrometer and SPD were compared at different concentrations. At high concentrations of $10^{-2}\,M$ and $10^{-3}\,M$, both the spectrometer and the SPD were able to resolve two closely spaced peaks, $1314\,cm^{-1}$ and $1364\,cm^{-1}$, and the spectrometer showed better resolvability. However, as the concentration decreased to a range where spectral features were no longer clearly visible on the spectrometer, the SPD demonstrated superior sensitivity. Certain peaks, such as $776\,cm^{-1}$, disappeared from traditional CCD spectra but remained visible on SPD spectra at $10^{-3}\,M$. For lower concentrations of R6G, the spectrometer fails to display spectral peaks, while the SPD continues to capture the peaks. The SPD demonstrates significantly higher sensitivity, revealing characteristic peaks down to concentrations as low as $10^{-10} M$ as shown in Fig. 3(d,f). As the concentration of R6G decreases further, the signal-to-noise ratio of the peaks obtained from the SPD progressively diminishes. The ability to resolve closely spaced peaks also decreases with lower concentrations. Specifically, peaks at $1314\,cm^{-1}$ and $1364\,cm^{-1}$ become indistinguishable below a concentration of $10^{-8}\,M$. Throughout the testing, the dark count from the SPD remained constant and was measured before and after each spectra collection. It is worth mentioning that the signal intensity tends to increase significantly as it approaches the laser excitation wavelength, even with the presence of multiple notch filters.
Fig. 3. The spectra of R6G at different concentrations obtained from (a) traditional CCD, (b) SERS (with $40\pm 5 nm$ AgNPs), (c) AOTF-based single photon detection with high concentrations of R6G, (d) single photon detection with low concentrations of R6G, (e) SERS (with $90\pm 5 nm$ AgNPs), and (f) Zoomed in SPD spectra of R6G from (d) at low concentrations $10^{-8}\,M$ (blue), $10^{-9}\,M$ (orange) and $10^{-10}\,M$ (black).
The SF of SPD appears to be comparable to the EF of SERS at relatively higher concentrations, while showing higher sensitivity at extremely low concentrations, as shown in Table 1. At $10^{-5} M$, while displaying similar EF and SF, peaks are more resolved from SERS measurement. Peaks with lower intensities, such as $1653\,cm^{-1}$, cannot be resolved below $10^{-5} M$ with florescence. SPD shows higher sensitivity at lower concentration at $10^{-8}\,M$ compared to SERS. The close two peaks at $1314\,cm^{-1}$ and $1364\,cm^{-1}$ can no longer be resolved below $10^{-8} M$. The signal to noise ratio of peaks such as $1188\,cm^{-1}$ and $1510\,cm^{-1}$ decreases along the concentration. The enhancement of signal intensity compared to the spectrometer at the same concentration demonstrates the SPD has superior sensitivity, as the SF and EF of individual peaks have the difference by factor of $10^3$. It needs to be noted that studies on the size of nanoparticles have been conducted before [40] that for Au(core)-Ag(shell) nanoparticles the 100 nm nanospheres have the strongest enhancement factor. The SERS results of the $90\pm 5\,nm$ AgNPs are listed below for comparison. The SERS using $90\pm 5\,nm$ AgNPs as seen in Fig. 3(f), at the concentration of $10^{-5}\,M$, the EF is $10^2$ higher than the 40nm AgNPs ( Fig. 3(b) ) for peak $612\,cm^{-1}$ and $776\,cm^{-1}$, due to the nano-particle size and peak being close to the excitation wavelength. The peaks at $612\,cm^{-1}$ and $776\,cm^{-1}$ are more resolved with 90nm AgNPs (Fig. 3(e)) with $10^{-5}\,M$ and $10^{-6}\,M$ R6G. The EF of other peaks at $10^{-5}\,M$ are similar for both sizes of nano-particles (Table 1). The EF from 40 nm and 90 nm AgNPs are comparable at the concentration of $10^{-8}M$. The similar enhancement in our study for both sizes of AgNPs are due to the ’hot spots’ on our substrates. The 90nm AgNPs substrates have more scattered distribution hot spots in the substrate. Recent publication from Ge’s group has shown that target molecules could be captured in the hot spots in AgNPs substrates [41]. This method could enhance the sensitivity of our AgNPs substrate but required advanced fabrication process, and the signal can be maintained for only 1-2 min. We made the choice on the substrate based on prior experience and stability of substrate.
Table 1. SF/EF of SPD/SERS with AgNPs of sizes 40 nm and 90 nm for various peaks at two R6G concentrations.
3.2 Time resolved single-photon counting
Subsequently, we performed time-resolved measurements using SPD detection within the same experimental setup. Our goal was to improve the signal-to-noise ratio while mitigating fluorescence interference. To accomplish this, we implemented two distinct techniques: (a) the numerical integration method utilized in time-correlated single-photon counting spectra, and (b) the time-gated and time-correlated single-photon counting method with an adjustable gated window. The numerical approach involved integrating photon counts from spectra associated with specific frequencies on the AOTF within predefined time intervals. These time windows were selected based on the insights derived from the 3D spectra, as depicted in Fig. 4(a), for the concentration of $10^{-5}$ $M$. These spectra provided information regarding the relationships among time, photon counts, and frequencies (corresponding to the Raman shift). In Fig. 4(b), we can observe the relationship between lifetime and photon counts for all Raman shifts, providing a side view of the data presented in Fig. 4(a). This side view indicates that the peak of the signal lands at the $\sim$9 ns mark, with the Raman signal and the fluorescence having a total temporal duration of $\sim$5 ns. The integration of photon counts was carried out using the Trapezoidal numerical integration method implemented in MATLAB. Integration of photon counts of 8.4-8.75 ns (Fig. 4(1)), 8.4-9.5 ns (Fig. 4(2)) and 8.4-11.9 ns (Fig. 4(3)) shows the spectra generated from the numerical method of different time duration.
Fig. 4. Time-correlated single-photon counting Raman spectroscopy after AOTF for R6G concentration of $10^{-5}M$. (a) SPD without gating and (b) side view of (a). (1) integrated photon count from 8.4-8.75 ns, (2) integrated photon count from 8.4-9.5 ns, and (3) integrated photon count from 8.4-11.9 ns.
Table 2 displays $SNR_{ave}$ for the purpose of result comparison, as shown in Eq. (3). At the same concentration of $10^{-5}$ $M$, the peaks from short time-integrated spectra have higher $SNR_{ave}$ than the peaks from SPD in Fig. 3(d). The $SNR_{ave}$ for 776 $cm^{-1}$ peak is 2.01, 1.12 and 0.92 for the time window of 8.4-8.75 ns, 8.4-9.5 ns and 8.4-11.9 ns respectively, compared to 0.6 in Fig. 3(d), an increase of 203${\% }$. Note that for peaks with weak signal such as 1653 $cm^{-1}$, spectra with 8.4-8.75 ns has $SNR_{ave}$ of 0.83, which still presents an increase of 62${\% }$ compared to SPD alone in Fig. 3(d). Spectra with 8.4-8.75 ns presents the best $SNR_{ave}$, with an average of 54${\% }$ of $SNR_{ave}$ decrease in 8.4-9.5 ns, and the lowest $SNR_{ave}$ for 8.4-11.9 ns with a further decrease of 32.1${\% }$. Note that the peaks at $1132cm^{-1}$ and $1653\,cm^{-1}$ have low SNR in Fig. 3(d) from SPD, which is similar to Fig. 4(3). With the smaller time window of 8.4-8.75 ns and 8.4-9.5 ns, the two peaks have better SNR and are more resolvable. As for the $1653cm^{-1}$, the SNR during the 0.35 ns time window surpasses the 3.5 ns time window by 261${\% }$.
Table 2. $SNR_{ave}$ of selected peaks by numerical integration method.
Figure 5(a-c) illustrates the spectra obtained from time-gated and time-correlated single-photon counting measurements utilizing a digital delay generator with pulse widths of 1 ns, 2 ns, and 3 ns, respectively. Similar to Table 2, in this case as well, we choose specific peaks to assess the $SNR_{ave}$ at $776\,cm^{-1},1188\,cm^{-1}, 1364\,cm^{-1},1510\,cm^{-1}$ and $1652 \,cm^{-1}$. The $SNR_{ave}$ of 1 ns is 3.50, while 2.11 at 2 ns and higher at 1.46 for 3 ns. These peak $SNR_{ave}$ values are detailed in Table 3, calculated using Eq. (3). A pulse width of 1 ns yielded the highest SNR, surpassing that of 2 ns by 65.8% on average and 3 ns by 139.7%. For the 1 ns delay width (Fig. 5(a)), minor peaks such as $1132\,cm^{-1}$ and $1654\,cm^{-1}$ exhibit better resolution and higher SNR compared to Fig. 3(d), in line with the integration method results. Additionally, the closely spaced peaks, $1314\,cm^{-1}$ and $1364\,cm^{-1}$, display improved SNR. Specifically, $1654\,cm^{-1}$ exhibits an SNR of 0.93, a 257% increase compared to the 3 ns delay time width, consistent with the SNR increase observed with the integration method. There is potential for further improvement with a reduction in the time window. Unfortunately, our digital delay generator’s limitations prevented us from narrowing the gated window any further. Nevertheless, this obstacle can be overcome by implementing the optical gating technique, utilizing ultra-fast picosecond gating, which leverages nonlinear up-conversion processes [36,42].
Fig. 5. Time-gated and time-correlated SPD spectra of $10^{-5}M$ R6G are presented for varying gate widths generated by the delay generator. (a) 1 ns, (b) 2 ns, and (c) 3 ns.
Table 3. $SNR_{ave}$ of selected peaks by time-gated and time-correlated method.
4. Discussion
It is important to note that our comparison is based on immobilized mono-dispersed spherical AgNPs and R6G as a model analyte. The SERS detection has been extensively researched, with reported EF reaching as high as $10^{15}$ using 40-80 nm Ag-coated TiO$_{\textrm {2}}$ nanotubes for methylene blue detection [43]. Other studies have demonstrated high EF using different nanostructures, such as Au dimers and trimers enclosed in a silica shell [44], Ag nanoprisms [45], and Au nanorods [46]. Additionally, advancements in SERS have led to higher EF and improved concentration detection limits, reaching down to $10^{-13} M$, attributed to the utilization of internally excited plasmon and azimuthal vector beam [11]. In our pursuit of attaining a comparable detection limit without relying on nanoparticles, our work represents an exploration into the utilization of the AOTF-based SPD method for weak Raman detection and the isolation of Raman signals from fluorescence.
Raman spectroscopy employing SPD has been investigated recently by other groups, with diverse optical detection methods. Instead of the AOTF-based SPD utilized in our study, single photon avalanche detector (SPAD) arrays in conjunction with gratings have emerged as a widely adopted alternative. Notably, Nissinen’s group introduced a method utilizing a $16\times 256$ element SPAD and a time-to-digital converter with gratings, successfully detecting Raman signals and reducing fluorescence in samples like olive oil, effectively extracting signals from a 2.5ns fluorescence background [47]. Li’s group presented a cost-effective approach using SPAD with a concave grating, observing two prominent peaks in rhodamine B [31]. Erdogan et al. designed a $512\times 16$ SPAD on a $0.13\mu$m CMOS line sensor, demonstrating detection capabilities in the blue region and near-IR with two individually optimized SPADs [48]. On the contrary, our approach utilizes AOTF and SPD for single-pixel detection, streamlining the system by eliminating the necessity for an array of detectors. Our method involves simplified data processing in comparison to multi-pixel SPAD arrays [49].
To the best of our knowledge, our study represents the initial study of the AOTF-based SPD technique for Raman detection, including a comparative analysis with SERS measurements and fluorescence reduction. Brimrose Corp. has recently enhanced AOTF technology to achieve improved resolution and an ultra-fast scanning rate ($\sim \,$25 $\mu$s per step) with a fiber-coupled version. This advancement enables the integration of our experimental setup on the fiber, offering a streamlined and cost-effective approach poised to improve resolution and acquisition in ongoing single-photon sensitive Raman studies.
5. Conclusion
In summary, a comparative study was conducted to investigate the AOTF based single photon detection technology for Raman spectroscopy. The measurement sensitivity was compared between a Raman spectrometer equipped with a nitrogen-cooled CCD, SERS, and a single photon counting module, using varying concentrations of R6G as the common Raman signal source. The results demonstrated that photon counting can significantly enhance sensitivity by up to eight orders of magnitude, achieving performance comparable to surface enhancement techniques. The study also incorporated time-resolved and time-gated measurements to improve the signal-to-noise ratio and minimize interference from fluorescent emission, while providing insights into the temporal dynamics of Raman scattering.
While there is still much work to be done following this comparative study, it is evident that AOTF-based single photon detection has demonstrated its potential for applications involving weak Raman signal detection. This includes scenarios with small Raman scattering cross-sections or limitations on the available pump laser power. By optimizing grating and wavelength schemes, Raman spectroscopy based on SPD holds promise for diverse applications such as standoff sensing and noninvasive tissue imaging [50,51].
For future studies, optical time-gating techniques utilizing nonlinear upconversion processes could be employed to achieve timing resolutions in the range of a few or sub picoseconds, as described in [42]. This would enable more detailed characterization of Raman scattering dynamics. Additionally, incorporating upconversion single photon imaging as outlined in [36], it would be possible to extend the current single-point Raman measurement to multiple points throughout the volume of the sample. With these advantages and prospects, single-photon sensitive, time-gated Raman spectroscopy could usher in a new ultrasensitive Raman-based chem/bio-sensing and imaging paradigm with a myriad of applications such as environmental monitoring, food and drinking water safety, cancer diagnosis, and planetary exploration.
Funding
ACC-New Jersey (Contract No. W15QKN-18-D-0040).
Acknowledgments
This work is supported by the ACC-New Jersey under Contract No. W15QKN-18-D-0040.
Disclosures
The authors declare no conflicts of interest.
Data availability
Data underlying the presented results in this paper are not publicly available at this time but may be obtained from the authors upon reasonable request.
References
1. Y. Wang, D. C. Alsmeyer, and R. L. McCreery, “Raman spectroscopy of carbon materials: structural basis of observed spectra,” Chem. Mater. 2(5), 557–563 (1990). [CrossRef]
2. T. Chen, A. Pal, J. Gao, et al., “Identification of Vertical and Horizontal Configurations for BPE Adsorption on Silver Surfaces,” J. Phys. Chem. C 119(43), 24475–24488 (2015). [CrossRef]
3. H. Chen, K. Liu, Y. Ma, et al., “Nanostructured sapphire optical fiber for sensing in harsh environments,” in Micro- and Nanotechnology Sensors, Systems, and Applications IX, vol. 10194 (SPIE, 2017), pp. 307–315.
4. O. Beyssac, “New trends in Raman spectroscopy: from high-resolution geochemistry to planetary exploration,” Elements 16(2), 117–122 (2020). [CrossRef]
5. H. J. Butler, L. Ashton, B. Bird, et al., “Using raman spectroscopy to characterize biological materials,” Nat. Protoc. 11(4), 664–687 (2016). [CrossRef]
6. A. Paudel, D. Raijada, and J. Rantanen, “Raman spectroscopy in pharmaceutical product design,” Adv. Drug Delivery Rev. 89, 3–20 (2015). [CrossRef]
7. A. Silge, K. Weber, D. Cialla-May, et al., “Trends in pharmaceutical analysis and quality control by modern Raman spectroscopic techniques,” TrAC, Trends Anal. Chem. 153, 116623 (2022). [CrossRef]
8. E. V. Efremov, F. Ariese, and C. Gooijer, “Achievements in resonance Raman spectroscopy: Review of a technique with a distinct analytical chemistry potential,” Anal. Chim. Acta 606(2), 119–134 (2008). [CrossRef]
9. R. Manoharan, K. Shafer, L. Perelman, et al., “Raman spectroscopy and fluorescence photon migration for breast cancer diagnosis and imaging,” Photochem. Photobiol. 67(1), 15–22 (1998). [CrossRef]
10. M. D. Sonntag, E. A. Pozzi, N. Jiang, et al., “Recent advances in tip-enhanced raman spectroscopy,” J. Phys. Chem. Lett. 5(18), 3125–3130 (2014). [CrossRef]
11. M. Liu, W. Zhang, F. Lu, et al., “Plasmonic tip internally excited via an azimuthal vector beam for surface enhanced raman spectroscopy,” Photonics Res. 7(5), 526–531 (2019). [CrossRef]
12. L. Zhang, W. Zhang, F. Lu, et al., “Azimuthal vector beam exciting silver triangular nanoprisms for increasing the performance of surface-enhanced raman spectroscopy,” Photonics Res. 7(12), 1447–1453 (2019). [CrossRef]
13. J. Langer, D. Jimenez de Aberasturi, J. Aizpurua, et al., “Present and future of surface-enhanced raman scattering,” ACS Nano 14(1), 28–117 (2020). [CrossRef]
14. S. Siddhanta, A. N. Kuzmin, A. Pliss, et al., “Advances in raman spectroscopy and imaging for biomedical research,” Adv. Opt. Photonics 15(2), 318–384 (2023). [CrossRef]
15. S. Nie and S. R. Emory, “Probing single molecules and single nanoparticles by surface-enhanced raman scattering,” Science 275(5303), 1102–1106 (1997). [CrossRef]
16. D. Pristinski, S. Tan, M. Erol, et al., “In situ SERS study of Rhodamine 6G adsorbed on individually immobilized Ag nanoparticles,” J. Raman Spectrosc. 37(7), 762–770 (2006). [CrossRef]
17. B. Sharma, R. R. Frontiera, A.-I. Henry, et al., “SERS: Materials, applications, and the future,” Mater. Today 15(1-2), 16–25 (2012). [CrossRef]
18. X. X. Han, R. S. Rodriguez, C. L. Haynes, et al., “Surface-enhanced raman spectroscopy,” Nat. Rev. Methods Primers 1(1), 87 (2022). [CrossRef]
19. S. He, D. Wu, S. Chen, et al., “Au-on-ag nanostructure for in-situ sers monitoring of catalytic reactions,” Nanotechnology 33(15), 155701 (2022). [CrossRef]
20. K. Kneipp, Y. Wang, H. Kneipp, et al., “Single molecule detection using surface-enhanced raman scattering (SERS),” Phys. Rev. Lett. 78(9), 1667–1670 (1997). [CrossRef]
21. S. Dong, D. He, Q. Zhang, et al., “Early cancer detection by serum biomolecular fingerprinting spectroscopy with machine learning,” eLight 3(1), 17 (2023). [CrossRef]
22. R. R. Jones, D. C. Hooper, L. Zhang, et al., “Raman techniques: fundamentals and frontiers,” Nanoscale Res. Lett. 14(1), 231 (2019). [CrossRef]
23. R. H. Hadfield, “Single-photon detectors for optical quantum information applications,” Nat. Photonics 3(12), 696–705 (2009). [CrossRef]
24. M. D. Eisaman, J. Fan, A. Migdall, et al., “Invited Review Article: Single-photon sources and detectors,” Rev. Sci. Instrum. 82(7), 071101 (2011). [CrossRef]
25. A. Rossetta, E. Slenders, M. Donato, et al., “The brighteyes-ttm as an open-source time-tagging module for democratising single-photon microscopy,” Nat. Commun. 13(1), 7406 (2022). [CrossRef]
26. N. Finlayson, A. Usai, G. E. Brown, et al., “Time-correlated single photon Raman spectroscopy at megahertz repetition rates,” Opt. Lett. 46(17), 4104 (2021). [CrossRef]
27. L. A. Jakob, W. M. Deacon, O. Hicks, et al., “Single photon multiclock lock-in detection by picosecond timestamping,” Optica 8(12), 1646–1653 (2021). [CrossRef]
28. L. Dai, J. Liu, K. Liang, et al., “Realization of a time-correlated photon counting technique for fluorescence analysis,” Biomed. Opt. Express 11(4), 2205–2212 (2020). [CrossRef]
29. T. Lipiäinen, J. Pessi, P. Movahedi, et al., “Time-gated raman spectroscopy for quantitative determination of solid-state forms of fluorescent pharmaceuticals,” Anal. Chem. 90(7), 4832–4839 (2018). [CrossRef]
30. J. Kostamovaara, J. Tenhunen, M. Kögler, et al., “Fluorescence suppression in raman spectroscopy using a time-gated cmos spad,” Opt. Express 21(25), 31632–31645 (2013). [CrossRef]
31. Z. Li and M. J. Deen, “Towards a portable raman spectrometer using a concave grating and a time-gated cmos spad,” Opt. Express 22(15), 18736–18747 (2014). [CrossRef]
32. M. Kögler and B. Heilala, “Time-gated raman spectroscopy – a review,” Meas. Sci. Technol. 32(1), 012002 (2020). [CrossRef]
33. M. Kögler, J. Itkonen, T. Viitala, et al., “Assessment of recombinant protein production in e. coli with time-gated surface enhanced raman spectroscopy (tg-sers),” Sci. Rep. 10(1), 2472 (2020). [CrossRef]
34. T. Talala, V. A. Kaikkonen, P. Keranen, et al., “Time-resolved Raman spectrometer with high fluorescence rejection based on a CMOS SPAD line sensor and a 573-nm pulsed laser,” IEEE Trans. Instrum. Meas. 70, 1–10 (2021). [CrossRef]
35. Z. Zhang, T. Peng, X. Nie, et al., “Entangled photons enabled time-frequency-resolved coherent raman spectroscopy and applications to electronic coherences at femtosecond scale,” Light: Sci. Appl. 11(1), 274 (2022). [CrossRef]
36. H. Zhang, S. Kumar, Y. M. Sua, et al., “Near-infrared 3d imaging with upconversion detection,” Photonics Res. 10(12), 2760–2767 (2022). [CrossRef]
37. S. Kumar, H. Zhang, S. Maruca, et al., “Mode-selective image upconversion,” Opt. Lett. 44(1), 98–101 (2019). [CrossRef]
38. P. Rehain, Y. M. Sua, S. Zhu, et al., “Noise-tolerant single photon sensitive three-dimensional imager,” Nat. Commun. 11(1), 921 (2020). [CrossRef]
39. M. Garikapati, S. Kumar, H. Zhang, et al., “Programmable spatiotemporal quantum parametric mode sorter,” Phys. Rev. Appl. 19(4), 044070 (2023). [CrossRef]
40. K. Sugawa, T. Akiyama, Y. Tanoue, et al., “Particle size dependence of the surface-enhanced Raman scattering properties of densely arranged two-dimensional assemblies of Au(core)–Ag(shell) nanospheres,” Phys. Chem. Chem. Phys. 17(33), 21182–21189 (2015). [CrossRef]
41. M. Ge, P. Li, G. Zhou, et al., “General surface-enhanced raman spectroscopy method for actively capturing target molecules in small gaps,” J. Am. Chem. Soc. 143(20), 7769–7776 (2021). [CrossRef]
42. A. Shahverdi, Y. M. Sua, L. Tumeh, et al., “Quantum parametric mode sorting: Beating the time-frequency filtering,” Sci. Rep. 7(1), 6495 (2017). [CrossRef]
43. M. Kadir, R. Nemkayeva, G. Baigarinova, et al., “SERS-active substrates based on Ag-coated TiO2 nanotubes and nanograss,” Phys. E 145, 115499 (2023). [CrossRef]
44. S. L. Kleinman, B. Sharma, M. G. Blaber, et al., “Structure enhancement factor relationships in single gold nanoantennas by surface-enhanced Raman excitation spectroscopy,” J. Am. Chem. Soc. 135(1), 301–308 (2013). [CrossRef]
45. N. Michieli, R. Pilot, V. Russo, et al., “Oxidation effects on the SERS response of silver nanoprism arrays,” RSC Adv. 7(1), 369–378 (2017). [CrossRef]
46. A. B. Serrano-Montes, D. Jimenez de Aberasturi, J. Langer, et al., “A general method for solvent exchange of plasmonic nanoparticles and self-assembly into SERS-active monolayers,” Langmuir 31(33), 9205–9213 (2015). [CrossRef]
47. I. Nissinen, J. Nissinen, P. Keranen, et al., “A 16 × 256 SPAD line detector with a 50-ps, 3-bit, 256-channel time-to-digital converter for Raman spectroscopy,” IEEE Sens. J. 18(9), 3789–3798 (2018). [CrossRef]
48. A. T. Erdogan, R. Walker, N. Finlayson, et al., “A CMOS SPAD line sensor with per-pixel histogramming TDC for time-resolved multispectral imaging,” IEEE J. Solid-State Circuits 54(6), 1705–1719 (2019). [CrossRef]
49. S. Burri, C. Bruschini, and E. Charbon, “LinoSPAD: a compact linear SPAD camera system with 64 FPGA-based TDC modules for versatile 50 ps resolution time-resolved imaging,” Instruments 1(1), 6 (2017). [CrossRef]
50. K. Kim and W. R. Wagner, “Non-invasive and non-destructive characterization of tissue engineered constructs using ultrasound imaging technologies: a review,” Ann. Biomed. Eng. 44(3), 621–635 (2015). [CrossRef]
51. K. Hanna, E. Krzoska, A. M. Shaaban, et al., “Raman spectroscopy: current applications in breast cancer diagnosis, challenges and future prospects,” Br. J. Cancer 126(8), 1125–1139 (2022). [CrossRef]
