Time-domain Technique Research Articles

Terahertz conductivity of two-dimensional materials: a review.

Two-dimensional (2D) van der Waals materials are shaping the landscape of next-generation devices, offering significant technological value thanks to their unique, tunable, and layer-dependent electronic and optoelectronic properties. Time-domain spectroscopic techniques at terahertz (THz) frequencies offer noninvasive, contact-free methods for characterizing the dynamics of carriers in 2D materials. They also pave the path toward the applications of 2D materials in detection, imaging, manufacturing, and communication within the increasingly important THz frequency range. In this paper, we overview the synthesis of 2D materials and the prominent THz spectroscopy techniques: THz time-domain spectroscopy (THz-TDS), optical pump THz probe (OPTP) technique, and optical pump--probe (OPP) THz spectroscopy. Through a coalescence of experimental findings, numerical simulation, and theoretical analysis, we present the current understanding of the rich ultrafast physics of technologically significant 2D materials: graphene, transition metal dichalcogenides, MXenes, perovskites, topological 2D materials, and 2D heterostructures. Finally, we offer a perspective on the role of THz characterization in guiding future research and in the quest for ideal 2D materials for new applications.

Time-domain direct capacitive coupling detection technique applicable inside a cavity structure for determination of crossing frequency in a transmission microwave frequency spectrum

Abstract The conventional crossing frequency method for plasma diagnostics measures transmission microwave frequency spectrum, called S_21 spectrum, using transmitting and receiving antennae. It can infer electron density (n_e) from the crossing frequency (f_cross) on which S_21 spectra with plasma generation and vaccum (no plasma generation) intersect. However, this method faces a challenge when it is used inside a cavity, especially a vacuum chamber, due to spike-like peaks in the S_21 spectrum with vacuum. Those peaks disturb the S_21 spectrum and make multiple crossing points, leading to difficulty in determining the f_cross. This paper proposes a method to remove those peaks, named time-domain capacitive coupling detection technique. For preliminary study, a three-dimensional electromagnetic wave simulation was employed and we found the coupling mechanism between antennae and optimum working condition. The proposed method is evaluated in an inductively coupled plasma system. Exepriment results showed the clear removal of spike-like peaks in S_21 spectrum with vacuum inside a cylindrical vacuum chamber. With plasma generation, the n_e from the proposed method shows a good agreement with that from the cutoff probe method, indicating the working in this method. Furthermore, we verified the operation at high pressure in which the cutoff method does not work. As a result, the time-domain direct capacitive coupling detection technique is a technique to measure electron density inside a vacuum chamber, especially at high pressure plasma diagnostics.

Photonic hook shaping achieved by the micro-nano fiber array based Janus cylindrical metalens

Abstract Near-field focusing of an electromagnetic wave on the assembly of hexagonally asymmetric arranged close-contact nanofibers into a cylindrical Janus metalens is considered for different fiber sizes and optical properties. We propose combining the meta-material concept with a photonic hook based on the finite-difference time-domain technique. Simulation results show that the cylindrical meta-photonic Janus structure produces the focal region of enhanced optical intensity, which exists in the spatial form of a localized structured light known as a photonic hook. A detailed study of all key parameters of a photonic hook depending on the Janus metalens topology and optical properties of the fiber filling is carried out. The structure conditions have been determined for the beam waist of a photonic hook that is less than the diffraction limit. This Janus cylindrical metalens may contribute to the versatile control of photonic hook generation in the applications of bio-photonics and optical microscopes.

A dielectric, thermodynamic and volumetric investigation for heteromolecular hydrogen bonding in 2-ethoxyethanol and ethanol binary mixture

ABSTRACT The dielectric, thermodynamic and volumetric measurements of neat 2-ethoxyethanol, neat ethanol and their binary mixtures have been carried out using time domain technique at 10°C–25°C with a variation of 5°C. Various dielectric parameters are calculated from complex dielectric spectra and are used to compute Kirkwood correlation factor, Bruggeman factor and activation thermodynamic parameters. These parameters are used to predict heteromolecular hydrogen bonding between associating molecules. The classical and non-classical type H-bonding interactions among the molecules are predicted using FT-IR spectroscopy.

Analytical modelling of halide perovskite material based hybrid biosensor to detect formalin using surface plasmon resonance strategy

The recurring consumption of formalin, a notorious preservative, can lead to a variety of lethal diseases and cause significant morbidity, highlighting the pressing need for its precise detection in developing nations. Hence, in this article, a Kretschmann-configuration-based hybrid surface plasmon resonance (SPR) biosensor is proposed and analysed numerically for formalin detection. The sensor heterostructure consists of five monolayers, namely BAK1 prism, WS2, silver, FASnI3 halide perovskite (HP), and 2D black phosphorous (BP). The influence of an additional metal oxide layer (ZnO, TiO2, SiO2, Al2O3, and MoO3) over the metallic layer is also investigated w.r.t. major performance indicators such as sensitivity, detection accuracy, quality factor, and figure of merit. Here, BP and HP jointly act as biomolecular recognition elements. Chitosan serves as a probe to react with formalin, which acts as the target molecule. Using the attenuated total reflection (ATR) concept and Fresnel equations, we’ve employed angular interrogation and transfer matrix approaches to detect the concentration of formalin by monitoring the variations in the minimum reflectance and maximum transmittance attributors in relation to SPR angle and SPR frequency (SPRF), respectively. The simulation findings demonstrate a minimal variation in SPR angle and SPRF for improper formalin sensing, confirming its absence in liquid solution. In contrast, the aforesaid attributors show significantly measurable changes when formalin is properly sensed, confirming its presence. We demonstrate that adding MoO3 over the Ag layer can enhance the detection accuracy of our primary HP-based SPR biosensor to a maximum of 55.6 %. The finite difference time domain (FDTD) technique is utilized to examine the distribution of electric fields within this biosensor structure to showcase the distinct contributions of HP and metal oxide. In the end, our simulation work is validated by a comparative study of the performance parameters with a few of the previous works on formalin detection.

Heart Sound Classification Using Harmonic and Percussive Spectral Features from Phonocardiograms with a Deep ANN Approach

Cardiovascular diseases (CVDs) are a leading cause of mortality worldwide, with a particularly high burden in India. Non-invasive methods like Phonocardiogram (PCG) analysis capture the acoustic activity of the heart. This holds significant potential for the early detection and diagnosis of heart conditions. However, the complexity and variability of PCG signals pose considerable challenges for accurate classification. Traditional methods of PCG signal analysis, including time-domain, frequency-domain, and time-frequency domain techniques, often fall short in capturing the intricate details necessary for reliable diagnosis. This study introduces an innovative approach that leverages harmonic–percussive source separation (HPSS) to extract distinct harmonic and percussive spectral features from PCG signals. These features are then utilized to train a deep feed-forward artificial neural network (ANN), classifying heart conditions as normal or abnormal. The methodology involves advanced digital signal processing techniques applied to PCG recordings from the PhysioNet 2016 dataset. The feature set comprises 164 attributes, including the Chroma STFT, Chroma CENS, Mel-frequency cepstral coefficients (MFCCs), and statistical features. These are refined using the ROC-AUC feature selection method to ensure optimal performance. The deep feed-forward ANN model was rigorously trained and validated on a balanced dataset. Techniques such as noise reduction and outlier detection were used to improve model training. The proposed model achieved a validation accuracy of 93.40% with sensitivity and specificity rates of 82.40% and 80.60%, respectively. These results underscore the effectiveness of harmonic-based features and the robustness of the ANN in heart sound classification. This research highlights the potential for deploying such models in non-invasive cardiac diagnostics, particularly in resource-constrained settings. It also lays the groundwork for future advancements in cardiac signal analysis.

Study of complex instability in a Thulium-doped figure-eight fiber laser including a polarization-imbalanced NOLM with no polarization control.

This study proposes a Thulium-doped figure-eight (F8) fiber laser design to be used as a platform to explore complex dynamics in passively mode-locked fiber laser systems. The proposed scheme incorporates a polarization-imbalanced nonlinear optical loop mirror (PI-NOLM) without a polarizer in the ring section. The absence of a polarizer prevents controlling the polarization state at the PI-NOLM input, which in turn allows the PI-NOLM switching power to vary dynamically during the laser operation. This leads to an intermittent noise-like pulse (NLP) operation, which is affected by complex instability in the form of non-periodic Q-switched-like behavior. Using real-time time-domain mapping techniques, we get a deep insight into the laser intricate dynamics, which involves not only NLPs, but also solitons and quasi-continuous-wave components with their subtle interactions. This study highlights the importance of the PI-NOLM in F8 laser schemes for the study and understanding of complex dynamics in Thulium-doped fiber lasers, which have been less extensively studied compared to other rare-earth-doped lasers. Our findings will contribute to the optimization and design of advanced laser systems for various scientific and industrial applications.

Gravitational Wave Ringdown Analysis Using the F -statistic

After the final stage of the merger of two black holes (BHs), the ringdown signal takes an important role in providing information about the gravitational dynamics in strong field. We introduce a novel time-domain (TD) approach, predicated on the F -statistic, for ringdown analysis. This method diverges from traditional TD techniques in that its parameter space remains constant irrespective of the number of modes incorporated. This feature is achieved by reconfiguring the likelihood and analytically maximizing over the extrinsic parameters that encompass the amplitudes and reference phases of all modes. Consequently, when performing the ringdown analysis under the assumption that the ringdown signal is detected by the Einstein Telescope, the parameter estimation computation time is shortened by at most 5 orders of magnitude compared to the traditional TD method. We further establish that traditional TD methods become difficult when including multiple overtone modes due to close oscillation frequencies and damping times across different overtone modes. Encouragingly, this issue is effectively addressed by our new TD technique. The accessibility of this new TD method extends to a broad spectrum of research and offers flexibility for various topics within BH spectroscopy applicable to both current and future gravitational wave detectors.

Noise-robust modal parameter identification and damage assessment for aero-structures

PurposeGround vibration testing is critical for aircraft design and certification. Fast relaxed vector fitting (FRVF) and Loewner framework (LF), recently extended to modal parameter extraction in mechanical systems to address the computational challenges of time and frequency domain techniques, are applied for damage detection on aeronautically relevant structures.Design/methodology/approachFRVF and LF are applied to numerical datasets to assess noise robustness and performance for damage detection. Computational efficiency is also evaluated. In addition, they are applied to a novel damage detection benchmark of a high aspect ratio wing, comparing their performance with the state-of-the-art method N4SID.FindingsFRVF and LF detect structural changes effectively; LF exhibits better noise robustness, while FRVF is more computationally efficient.Practical implicationsLF is recommended for noisy measurements.Originality/valueTo the best of the authors’ knowledge, this is the first study in which the LF and FRVF are applied for the extraction of the modal parameters in aeronautically relevant structures. In addition, a novel damage detection benchmark of a high-aspect-ratio wing is introduced.

Electronic vs phononic thermal transport in Cr-doped V2O3 thin films across the Mott transition

Understanding the thermal conductivity of chromium-doped V2O3 is crucial for optimizing the design of selectors for memory and neuromorphic devices. We utilized the time-domain thermoreflectance technique to measure the thermal conductivity of chromium-doped V2O3 across varying concentrations, spanning the doping-induced metal–insulator transition. In addition, different oxygen stoichiometries and film thicknesses were investigated in their crystalline and amorphous phases. Chromium doping concentration (0%–30%) and the degree of crystallinity emerged as the predominant factors influencing the thermal properties, while the effect of oxygen flow (600–1400 ppm) during deposition proved to be negligible. Our observations indicate that even in the metallic phase of V2O3, the lattice contribution is the dominant factor in thermal transport with no observable impact from the electrons on heat transport. Finally, the thermal conductivity of both amorphous and crystalline V2O3 was measured at cryogenic temperatures (80–450 K). Our thermal conductivity measurements as a function of temperature reveal that both phases exhibit behavior similar to amorphous materials, indicating pronounced phonon scattering effects in the crystalline phase of V2O3.

Monitoring the gas metal arc additive manufacturing process using unsupervised machine learning

The study aimed to assess the performance of several unsupervised machine learning (ML) techniques in online anomaly (The term “anomaly” is used here to indicate a departure from expected process behavior which may indicate a quality issue which requires further investigation. The term “defect detection” has often been used previously but the specific imperfection is often indirectly inferred.) detection during surface tension transfer (STT)-based wire arc additive manufacturing. Recent advancements in quality monitoring for wire arc manufacturing were reviewed, followed by a comparison of unsupervised ML techniques using welding current and welding voltage data collected during a defect-free deposition process. Both time domain and frequency domain feature extraction techniques were applied and compared. Three analysis methodologies were adopted: ML algorithms such as isolation forest, local outlier factor, and one-class support vector machine. The results highlight that incorporating frequency analysis, such as fast Fourier transform (FFT) and discrete wavelet transform (DWT), for feature extraction based on general frequency response and defined bandwidth frequency response, significantly improves performance, reflected in a 14% increase in F2 score, compared with time-domain features extraction. Additionally, a deep learning approach employing a convolutional autoencoder (CAE) demonstrated superior performance by processing time-frequency domain data stored as spectrograms obtained through short-time Fourier transform (STFT) analysis. The CAE method outperformed frequency domain analysis and traditional ML approaches, achieving an additional 5% improvement in F2-score. Notably, the F2-score (The F2 score is the weighted harmonic mean of the precision and recall (given a threshold value). Unlike the F1 score, which gives equal weight to precision and recall, the F2 score gives more weight to recall than to precision.) increased significantly from 0.78 in time domain analysis to 0.895 in time-frequency analysis. The study emphasizes the potential of utilizing low-cost sensors to develop anomaly detection modules with enhanced accuracy. These findings underscore the importance of incorporating advanced data processing techniques in wire arc additive manufacturing for improved quality control and process optimization.

High-Performance Visible-to-SWIR Photodetector Based on the Layered WS2 Heterojunction with Light-Trapping Pyramidal Black Germanium.

This study presents a layered transition metal dichalcogenide/black germanium (b-Ge) heterojunction photodetector that exhibits superior performance across a broad spectrum of wavelengths spanning from visible (vis) to shortwave infrared (SWIR). The photodetector includes a thin layer of b-Ge, which is created by wet etching of germanium (Ge) wafer to form submicrometer pyramidal structures. On top of this b-Ge layer, the WS2 thin film is deposited using pulsed laser deposition. In comparison to conventional germanium, b-Ge absorbs about 25% more light between 850 and 1750 nm wavelengths. The WS2/b-Ge photodetector has a peak photoresponsivity of 0.65 A/W, which is more than twice the photoresponsivity of the WS2/Ge photodetector at 1540 nm. Additionally, it shows better responsivity and response speed compared with other similar state-of-the-art photodetectors. Such an improvement in the performance of the device is credited to the light-trapping effect enabled by the germanium pyramids. Theoretical simulations employing the finite-difference time-domain technique help validate the concept. This novel photodetector holds promise for efficient detection of light across the vis to SWIR spectrum.

New oscillatory features of electrodermal activity signals evaluated in automated mental workload monitoring

Oscillatory components are discriminative features of mental workload monitoring in electrodermal activity (EDA) signals. Currently, most feature extraction techniques mainly focus on time domain or linear techniques but rarely consider the non-stationary nature, individual differences, and recording noise. This study has proposed a new time–frequency feature extraction method based on Tunable Q-factor Wavelet Transform providing an effective quantification of oscillatory behavior of a single transient. The extracted features have been classified using a recurrent neural network with the advantage of dynamic mapping procedure. The proposed method has been evaluated using EDA data acquired during an arithmetic task of five workload levels with both subtle and distinct differences. In a comparative study, the effects of several decomposition parameters and cognitive factors have been explored. Experimental results have shown that it achieves an average accuracy rate of 98.52% for three workload levels discrimination by optimizing the number of features to 3 (features extracted from a low frequency sub-band) from 15 (features extracted from five frequency sub-bands), i.e., 80% feature reduction. The greatest contribution coming fromthe smallest frequency sub-bands has also been obtained. This method is superior to other existing methods in separating 3 to 5 workload levels with both distinct and subtle differences extracting effective oscillatory characteristics. Considering the merits of a simple feature extraction procedure and cost-effectiveness of a single lead EDA signal as well as high discriminability between several workload levels, it provides a trade-off between performance and computational complexity, thus demonstrating its potential for online human-error prevention systems.

Facile modification method for the controlled synthesis of dumbbell-like gold nanoparticles (AuNDBs) for application in detecting glucose using the SERS method

Dumbbell-like nanoparticles, previously known for their excellent light absorption and dispersion, have diverse applications, especially in SERS. Herein, we have explored a fast and novel controlled synthesis approach to achieve a high formation of AuNDBs. Precise control of precursor amounts and seed addition order halted seed growth at the AuNDB formation stage, preventing further elongation into rod-like structures. The AuNDBs were functionalized with 4-mercaptobenzoic acid (4-MBA) to create SERS nanosubstrates and deposited on the glass slide through 3-mercaptopropyl (dimethoxy)methylsilane by the spin-coating technique. The detection of d-glucose was achieved by measuring the signal decrease of the 4-MBA Raman probe molecules on the nanosubstrates at different trace concentrations of the analyte. These novel SERS substrates have the potential to provide an enhancement factor (EF) of 2.41 × 107 and a limit of detection (LOD) as low as 1.17 nmol/L. The electromagnetic field distribution of AuNDBs with many arrangements surrounded by air and d-glucose solutions was also determined by using the finite-difference time-domain technique (FDTD). The FDTD results confirmed that d-glucose on the SERS substrates caused the local electromagnetic field to degrade, resulting in the degree of the Raman signal. Our findings suggest that dumbbell-shaped SERS substrates can effectively detect very low levels of glucose, making them ideal for designing novel anisotropic nanoparticle-functionalized surfaces with ease.

Tungsten-Doped ZnO as an Electron Transport Layer for Perovskite Solar Cells: Enhancing Efficiency and Stability.

This study delves into enhancing the efficiency and stability of perovskite solar cells (PSCs) by optimizing the surface morphologies and optoelectronic properties of the electron transport layer (ETL) using tungsten (W) doping in zinc oxide (ZnO). Through a unique green synthesis process and spin-coating technique, W-doped ZnO films were prepared, exhibiting improved electrical conductivity and reduced interface defects between the ETL and perovskite layers, thus facilitating efficient electron transfer at the interface. High-quality PSCs with superior ETL demonstrated a substantial 30% increase in power conversion efficiency (PCE) compared to those employing pristine ZnO ETL. These solar cells retained over 70% of their initial PCE after 4000 h of moisture exposure, surpassing reference PSCs by 50% PCE over this period. Advanced numerical multiphysics solvers, employing finite-difference time-domain (FDTD) and finite element method (FEM) techniques, were utilized to elucidate the underlying optoelectrical characteristics of the PSCs, with simulated results corroborating experimental findings. The study concludes with a thorough discussion on charge transport and recombination mechanisms, providing insights into the enhanced performance and stability achieved through W-doped ZnO ETL optimization.

Modal group refractive index measurement of few-mode fibers based on time-domain cross-correlation

We propose a measurement method based on the time-domain cross-correlation technique, combined with the cut-back method, enabling the measurement of group refractive indices (n g ) in few-mode fibers (FMF). A Mach–Zehnder interferometric system, equipped with high-precision and extensive range delay devices, is established. The system records off-axis holograms of spatial reference light at various delays interfering with the emitted light from the fiber under test. The interference energy is extracted from these holograms, and a time-domain mode energy curve is developed utilizing the principle of cross correlation. Optimal holograms at each of the curve peaks are used to reconstruct the modal field distribution, effectively separating and accurately identifying each mode within the FMF. By integrating the cut-back method, the n g corresponding to each mode is calculated based on the changes in group delay before and after fiber cutting. The n g of modes in the two-mode fibers was measured and the differential group delay calculated from the measurement agrees with the manufacturer’s specifications. The measured n g of a standard single-mode fiber aligns with the manufacturer’s specifications. Furthermore, the n g of the higher-order modes in four-mode fibers were measured by exciting them at different angles and validating the wave optics theory that the n g of the fiber modes is independent of the excitation angle. This method can simultaneously measure the n g of several modes in a fiber, providing support for the development and application of FMFs.

Enhanced photoacoustic signal processing using empirical mode decomposition and machine learning

ABSTRACT In this study, we propose a robust photoacoustic (PA) signal processing framework for a material independent defect detection using empirical mode decomposition (EMD) and machine learning algorithms. First, a database of the PA signals with 960 samples has been obtained from aluminium, iron, plastic and wood materials using a laser, microphone and data acquisition board-based PA apparatus. Second, the EMD based time and time-frequency domain techniques are proposed to extract robust cross-material feature space focusing on laser induced acoustic signal, and the decomposed intrinsic mode (IMF) with 14 extracted features are performed on totally 960 samples PA signals to evaluate k-nearest neighbour (k-NN), decision tree (DT) and support vector machine (SVM) classifiers. Inter- material and cross-material evaluations are performed, and the accuracy rates up to 100% for SVM and 97.77% for k-NN are yielded.

Time-domain diffuse optical imaging technique for monitoring rheumatoid arthritis disease activity: experimental validation in tissue-mimicking finger phantoms

Objective. Effective treatment within 3–5 months of disease onset significantly improves rheumatoid arthritis (RA) prognosis. Nevertheless, 30% of RA patients fail their first treatment, and it takes 3–6 months to identify failure with current monitoring techniques. Time-domain diffuse optical imaging (TD-DOI) may be more sensitive to RA disease activity and could be used to detect treatment failure. In this report, we present the development of a TD-DOI hand imaging system and validate its ability to measure simulated changes in RA disease activity using tissue-mimicking finger phantoms. Approach. A TD-DOI system was built, based on a single-pixel camera architecture, and used to image solid phantoms which mimicked a proximal interphalangeal finger joint. For reference, in silico images of virtual models of the solid phantoms were also generated using Monte Carlo simulations. Spatiotemporal Fourier components were extracted from both simulated and experimental images, and their ability to distinguish between phantoms representing different RA disease activity was quantified. Main results. Many spatiotemporal Fourier components extracted from TD-DOI images could clearly distinguish between phantoms representing different states of RA disease activity. Significance. A TD-DOI system was built and validated using finger-mimicking solid phantoms. The findings suggest that the system could be used to monitor RA disease activity. This single-pixel TD-DOI system could be used to acquire longitudinal measures of RA disease activity to detect early treatment failure.

Time-domain diffuse optical imaging technique for monitoring rheumatoid arthritis disease activity: theoretical development and in silico validation

Objective. Effective early treatment—within 3–5 months of disease onset—significantly improves rheumatoid arthritis (RA) prognosis. Nevertheless, 1 in 3 patients experiences treatment failure which takes 3–6 months to detect with current monitoring techniques. The aim of this work is to develop a method for extracting quantitative features from data obtained with time-domain diffuse optical imaging (TD-DOI), and demonstrate their sensitivity to RA disease activity. Approach. 80 virtual phantoms of the proximal interphalangeal joint—obtained from a realistic tissue distribution derived from magnetic resonance imaging—were created to simulate RA-induced alterations in 5 physiological parameters. TD-DOI images were generated using Monte Carlo simulations, and Poisson noise was added to each image. Subsequently, each image was convolved with an instrument response function (IRF) to mimic experimental measurements. Various spatiotemporal features were extracted from the images (i.e. statistical moments, temporal Fourier components), corrected for IRF effects, and correlated with the disease index (DI) of each phantom. Main results. Spatiotemporal Fourier components of TD-DOI images were highly correlated with DI despite the confounding effects of noise and the IRF. Moreover, lower temporal frequency components ( ⩽ 0.4 GHz) demonstrated greater sensitivity to small changes in disease activity than previously published spatial features extracted from the same images. Significance. Spatiotemporal components of TD-DOI images may be more sensitive to small changes in RA disease activity than previously reported DOI features. TD-DOI may enable earlier detection of RA treatment failure, which would reduce the time needed to identify treatment failure and improve patient prognosis.

Frequency-Domain Low-Wavenumber Hyperspectral Stimulated Raman Scattering Microscopy.

In recent years, stimulated Raman scattering (SRS) microscopy has experienced rapid technological advancements and has found widespread applications in chemical analysis. Hyperspectral SRS (hSRS) microscopy further enhances the chemical selectivity in imaging by providing a Raman spectrum for each pixel. Time-domain hSRS techniques often require interferometry and ultrashort femtosecond laser pulses. They are especially suited to measuring low-wavenumber Raman transitions but are susceptible to scattering-induced distortions. Frequency-domain hSRS microscopy, on the other hand, offers a simpler optical configuration and demonstrates high tolerance to sample scattering but typically operates within the spectral range of 400-4000 cm-1. Conventional frequency-domain hSRS microscopy is widely employed in biological applications but falls short in detecting chemical bonds with a weaker vibrational energy. In this work, we extend the spectral coverage of picosecond spectral-focusing hSRS microscopy to below 100 cm-1. This frequency-domain low-wavenumber hSRS approach can measure the weaker vibrational energy from the sample and has a strong tolerance to sample scattering. By expanding spectral coverage to 100-4000 cm-1, this development enhances the capability of spectral-domain SRS microscopy for chemical imaging.