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A Novel Color Modulation Analysis Strategy through Tunable Multiband Laser for Nanoparticle Identification and Evaluation Xuan Cao, Gang Lei, Jingjing Feng, Qi Pan, Xiaodong Wen, and Yan He Anal. Chem., Just Accepted Manuscript • DOI: 10.1021/acs.analchem.7b03636 • Publication Date (Web): 15 Jan 2018 Downloaded from http://pubs.acs.org on January 17, 2018

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Analytical Chemistry

A Novel Color Modulation Analysis Strategy through Tunable Multiband Laser for Nanoparticle Identification and Evaluation Xuan Cao,†, § Gang Lei,‡ Jingjing Feng,‡ Qi Pan,‡ Xiaodong Wen,‡ Yan He*, †, ‡ †

State Key Laboratory of Chemo/Biosensing and Chemometrics, College of Chemistry and Chemical Engineering, College of Biology, Hunan University, Changsha, Hunan 410082, P. R. China ‡ Department of Chemistry, Tsinghua University, Beijing, 100084, P. R. China § Institute of Pharmacy and Pharmacology, University of South China, Hengyang, Hunan 421001, P. R. China *Email: [email protected] ABSTRACT: Creating color difference and improving the color resolution in digital imaging is crucial for better application of color analysis. Herein, a novel color modulation analysis strategy was developed by using a homemade tunable multiband laser illumination device, in which the portions of R, G, and B components of the illumination light are discretionarily adjustable, and hence the sample color could be visually modulated continuously in the RGB color space. Through this strategy, the color appearance of single gold nanorods (AuNRs) under dark-field microscopy, was migrated from the spectrally insensitive red region to the spectrally sensitive green-yellow-red region. Unlike the traditional continuous-wave light source illumination, wherein the small spectral variations in the samples within a narrow spectral range are averaged by the whole spectrum of the light source, leading to little color difference, the application of sharp, multiband laser illumination could enlarge the color separation between samples, thus resulting in high spectral sensitivity in color analysis. By comparing the corresponding color evolution processes of different samples as the multiband combination of the laser illumination was changed, more efficient color separation of AuNRs was achieved. With this instrument and single Ag@AuNRs as the sulfide probe, we achieved high throughput and highly sensitive detection of sulfide at a detection limit of 0.1 nM, a more than two orders of magnitude improvement compared to the previous color sensing scheme. This strategy could be utilized for nanoparticle identification, evaluation and determination in biological imaging and biochemical analysis.

For species identification, color analysis is probably the simplest detection method and has been widely used in physics, chemistry, materials science,1-6 and even some advanced fields including robotics and artificial intelligence.7, 8 Experts estimated that about ten millions of colors could be distinguished by naked eyes,9 which forms the basis of sensitive color detection. Essentially, the application of color analysis is dependent on the extraction of optical information of the sample to achieve chromatic differentiation through naked eyes or a digital photographic chip. Compared to the conventional spectrometer detection, which is generally expensive and difficult to be miniaturized,10-12 digital color analysis have many advantages such as simplicity, low cost, and high throughput.13-19 For instance, Long’s group developed a facile, real-time and low cost method for estimating the diameter of single gold nanoparticles through its scattering light color, in which the RGB (red, green, and blue) chrominance information from the dark-field image could be directly converted into the diameters of the gold nanoparticles.13 Nevertheless, digital color analysis also has some intrinsic shortcomings. Primarily, color sensing is usually not efficient to differentiate the samples with small spectral variations due to the nonlinearity of color response. Moreover, since color variation is most sensitive in the 540-590 nm yellow-green wavelength range, and insensitive to spectral change in other regions,20 e.g. the 600-700 nm red region, the application of digital colorimetric detection is generally very limited. Given the intrinsic short-

comings of color sensing, people have been more inclined to develop innovative spectral imaging approaches with better performance.21 A new strategy that could expand the applicable spectral range of color sensing with high sensitivity is highly desired in the digital color imaging field. Conceptually, color is a kind of subjective sensation stimulated by the differential signals detected by three type of optical receptors (cone cells) on the retina of human eyes in response to the visible light. To quantify color change, chromatists came up with the CIE (International Commission on Illumination) 1931 chromaticity diagram, and later its variation and improvements. According to the chromaticity diagrams, most of the accessible color can be reproduced by using the linear combination of three primary colors.22 To make the acquired digital image be displayed as a colorful picture consistent with the human vision, an image processing pipeline or “color engine” must be executed after the photons collected from the sample hit the surface of the color CCD or CMOS image sensor. Different from spectral measurement with a spectrometer, both the spectral and the intensity distribution of the light source play important roles that can significantly affect the appearance of the sample color. As a result, the RGB color sensor may not be able to detect color change induced by small spectral difference in samples under common white light illumination conditions. To tackle this problem, some research groups managed to amplify the color difference or the magnitude of signal change through digital manipula-

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tion of the RGB values obtained from the image sensor by applying certain data processing algorithms.23, 24 However, software manipulation just produces pseudo-colors without physical bases and could be erratic and unreliable. Previously, we established a physical method for color difference amplification by applying the combination of three single wavelength (473/532/635 nm) lasers as the light source and adjusting their relative intensity contribution.25 In this work, we further extend the controlled multiple narrow band illumination strategy by using a dynamically tunable multiband illumination device (DTMI) for color modulation. The idea of this device is to select a set of narrow spectral bands from a supercontinuum laser by using a movable set of narrow slits. Compared to lasers with fixed wavelength, the new method allows both the spectral and the intensity composition of the illumination light to be discretely controlled, making it possible to swiftly change the color appearance of the sample physically and reproducibly, and thus improve the color sensing capability of the imaging system. As a demonstration in single particle plasmonic imaging, we showed that gold nanorods (AuNRs) of different aspect ratios, with their spectral maximum of longitudinal resonance in the range of 610 nm to 660 nm and all appearing red color under conventional white light illumination, could experience a color change from red to green in different manner under DTMI. This strategy could be utilized for simple and high throughput color analysis of nanoparticles that can only be accomplished with a spectrometer before, and is promised to find wide applications in chemical and biological sensing.

EXPERIMENTAL SECTION Instrument Setup. The schematic diagram of the homemade imaging system was shown in Figure 1. A supercontinuum (SC) white light laser (EXB-6) purchased from NKT Photonics and with a spectral range from ～430 nm to ～2400 nm was used as the illumination source. The total power in the visible range was

Figure 1. Schematic diagram of the tunable multiband laser imaging system for color modulation analysis. (A) The arrangement of the instrument. S: shutter; SP: short pass filter; AL1, AL2: achro-

matic lens; M: reflection mirror; ACL1, ACL2, ACL3: achromatic cylindrical lens; IL: illumination lens; OL: objective lens; RL: relay lens system. (B) The design of the two homemade masks and the normalized spectra of the laser light before (blue line) and after (red lines) the two masks. The detailed engineering drawings of the two masks including the slit dimensions were provided in Figure S1.

~15% of the total laser output. The diameter of the SC laser light was about 0.4 mm. A laser shutter was placed to let the light pass during the experiment. A variable band-pass filter was equipped to control the wavelength range of the laser light from 430 nm to 700 nm. Two apochromatic lens (AL) (with focal length f1 = 30 mm and f2 = 150 mm) were then used to reduce chromatic aberration and expand the diameter of the SC laser beam to about 2 mm. After passing through the beam expander and a spatial filter, the SC laser light passed through a wavelength selector including four equilateral prisms and a homemade mask composed of a set of narrow slits to create a tunable multiband laser beam. Subsequently, the emergent laser beam was shaped into a planar sheet by two groups of achromatic cylindrical lenses (ACL). The optical axes of the first group of ACL (with focal length f3 = 50 mm and f4 = 250 mm) and the second group of lens (with the focal length of ACL f5 = 50 mm and the focal length of illumination objective (IL) f6 = 18 mm) were perpendicular and parallel to the sample, respectively. The final light sheet beam was incident to the sample at normal angle, and the distributions of laser power in the y and z directions are Gaussian. When samples were placed into the irradiation window, the scattering light were collected by a Nikon 20×/0.75 (NA) Plan Apochromat microscopy objective lens (OL) and the scattering image was acquired by an Olympus DP73 color CMOS camera. The obtained images were analyzed with either Image J or MATLAB. The UV-vis absorption of AuNRs and Ag@AuNRs solution were measured with a UV-1800 spectrometer (Shimazdu, Japan). Materials and Reagents. Silver nitrate (AgNO3 AR, 99.8%) and hexadecyltrimethyl ammoniumbromide (CTAB AR, 99.0%) were purchased from Sinopharm Chemical Reagent Co. Ltd. (Shanghai, China). Chloroauric acid (HAuCl4 AR, 99.9%) and polyvinyl pyrrolidone (MW = 29000) were obtained from Sigma-Aldrich (American). Ascorbic acid (AA AR, 99.0%) was obtained from Alfa Aesar chemicals Co. Ltd. (Shanghai, China). Sodium hydrosulfide (NaHS, 70%) was purchased from Adamas Reagent Co. Ltd. (Shanghai, China). Sodium hydroxide (NaOH AR, 96.0%) was obtained from Xilong chemical engineering Co. Ltd. (Shantou, China). Ammonia solution (NH3·H2O AR, 25%) was purchased from Beijing Reagent Co. Ltd. (Beijing, China). Milli-Q purified water (18.2 MΩ at 25 ℃) was used throughout. Preparation of AuNRs with Different Aspect Ratios. The AuNRs were prepared according to the previous reference.26 In brief, 24 µl of ice-cold 0.010 M NaBH4 was added to a 4 ml solution containing 0.1 M CTAB and 0.25 mM HAuCl4 under stirring. The obtained AuNP seed solution was stirred for another 2 min and then kept at 28 ℃ for at least 2 h prior to use. In 40 ml of 0.1 M CTAB solution, 825 µl of 24.28 mM HAuCl4, 400 µl of 1 M HCl, 1 ml of 4 mM AgNO3 and 210 µl of 0.1 M ascorbic acid (AA) were added in order. After the solution color changed from bright yellow to colorless, 140 µl of the AuNP seed solution was added and the mixture was shaken for 20-30 seconds, then kept at 29 ℃ undisturbed for 4


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Analytical Chemistry h. The excess reagents were removed via centrifugation at 8000 rpm twice, followed by washing with DI water twice. To acquire the AuNRs with different aspect ratios, 2 ml of the original AuNR solution was added into an etching solution containing 100 µl of 0.1 M NH4Br, 100 µl of 0.5 M HCl and 500 µl of 0.1 mM NaNO2, followed by rapidly shaking the bottle for 20 seconds. Then, the solution was allowed to react gradually and its spectrum was monitored by using a UV-Vis spectrometer. Finally, the reaction ceased after the addition of 1 mL of 1 M ammonium hydroxide. The excess reagents were removed via centrifugation at 8,000 rpm twice, followed by washing with DI water twice. The aspect ratios of AuNRs were controlled by the reaction time. Preparation of Ag@AuNRs. The gold nanorod-silver coreshell nanorods (Ag@AuNRs) were prepared using the seededgrowth method with some modifications.27 Firstly, 2 ml of purified AuNRs solution was mixed with 2 ml of 3% polyvinyl pyrrolidone aqueous solution, after that the freshly prepared ammoniacal silver nitrate (30 µl, 5 mM) was added to the mixture with stirring, followed by the addition of ascorbic acid solution (150 µl,10 mM). Finally, the solution was placed into a water bath and kept undisturbed at 30 ℃ until the color change was completed. Preparation of Gel Samples. The samples of AuNRs or Ag@AuNRs for gel imaging experiments were prepared as follow: firstly, four pieces of square capillary with 1 cm long and 0.38 mm thickness were pasted on the glass slide to produce a 1 cm region as the flow cell and the observation window. Then, 100 µL of purified nanorod solution was mixed with fresh hot 10 ml of 0.2% low melting agarose solution, and 10 µL of the mixture was injected into the flow cell. After the agarose solution cooled down and became a soft gel, the sample was covered with a 10×22 mm cover glass carefully and the space outside the flow cell was filled with water or oil before observation. Detection of Hydrogen Sulfide. The prepared Ag@AuNRs were used as the probe for hydrogen sulfide, and Na2S aqueous solution were used as the sulfide source. Firstly, we moved the mask (mask A) to a proper position which enabled the best color separation of the original Ag@AuNRs and those reacted with Na2S. Subsequently, samples of Ag@AuNRs reacted with 0.1-100 nM Na2S aqueous solution were prepared by dropping 10 µL of the reaction liquid on a clean glass slide. After the reaction liquid was air dried, the Ag@AuNRs were immobilized on the surface of the glass slide. Finally, the prepared samples were placed under the microscope for observation. The image analysis and statistical analysis of single Ag@AuNRs reacted with 0.1-100 nM Na2S was accomplished by a MATLAB code. For comparison, the conventional UVvis- and dark-field microscopy-based sulfide detections were also performed.

RESULTS AND DISCUSSION Color Modulation Mechanism. Different from spectral detection, color sensing is a complicated process that involves space transform from the physical spectral space to the perceptive color space. In brief, upon being illuminated by some light sources, a sample will absorb or scatter certain amount of light with its characteristic spectral distribution. After collected by the optical detection system, the transmitted or emitted light hits the color imaging sensor such as a CCD or CMOS chip with three color channels (R, G, and B) to acquire the color

information embedded in the spectral convolution of the sample and

Figure 2. Scheme of the color reproduction process under the traditional light source illumination (A, blue arrows) and the tunable laser multiband illumination (B, red arrows).

the light source. This is usually implemented through a Bayer color filter array (CFA) placed on top of the monochrome sensor chip, where each pixel is designed to detect only one of the three primary colors. When light impinges onto the sensor chip surface, every pixel will acquire an integral value on one of the three primaries, which can be quantitatively described as:28 ∞

R0 = ∫ I 0 (λ ) s0 (λ ) Dr (λ )d λ 0

∞

G0 = ∫ I 0 (λ ) s0 (λ ) Dg (λ )d λ 0

∞

B0 = ∫ I 0 (λ ) s0 (λ ) Db (λ )d λ 0

where I0 (λ) is the power spectral distribution function of the light source; s0 (λ) is the characteristic spectral response of the sample; and Dr (λ), Dg (λ), and Db (λ) are the spectral sensitivity curves of the red, green, and blue color channels of the CFA, respectively. After correction of the device-dependent reading errors，the obtained R0, G0, or B0 was converted to R/G/Braw values. Then a demosaicing process is utilized to convert R/G/Braw to RGBsensor triplets, followed by a physical to perceptive colorspace transformation process to associate each RGBsensor triplet values at every pixel to the corresponding XYZ tristimulus triplet in the RGBlinear space that matches human’s visual perception. Finally, a camera-specific gammacorrection and/or tone-mapping algorithm is applied to relate the intensity response of the camera to the non-linear response of the human vision to the luminance before the color image is exhibited on the screen by the display device through color reproduction. According to the color formation mechanism, the origin of the color information acquired by the color CCD or CMOS image sensor can be traced back to the convolution of the raw scattering spectra in the three R/G/B channels. The color appearance is decided collaboratively by the power spectral distribution function of the light source, the characteristic spectral response of the sample and the spectral sensitivity curves of the R/G/B channels of the CFA. Hence, in the case that the spectra of the sample and the CFA of the color imaging sensor are invariant, the color appearance is determined by the power spectral distribution function of the light source. For example, the illumination of the traditional optical dark-field microscopy is provided by a halogen-tungsten lamp having a continuous power spectral distribution. Since the sample passively responds to the whole spectral range of the light source, the acquired convoluted spectra of the sample and the source light (raw scattering spectra) will also have a continuous distribution (Figure 2A). After spectral integration in the three R/G/B channels, the small spectral difference in the samples within a



narrow spectral range is averaged, which may lead to the decrease of the corresponding color resolution. However, if the sample is illuminated by a multiband light, the acquired raw scattering spectra will also contain multiple sharp bands (Figure 2B). If the positions of the multibands are chosen properly, the small spectral differences in the samples could be mostly preserved even after spectral integration, hence improving the color resolution. Moreover, by continuously adjusting the spectral and intensity composition of the multiband light, arbitrary color modulation of the sample could be accomplished. Wavelength Selector and Mask Design. In the previous work, we have achieved the color difference amplification through three single wavelength (473/532/635 nm) lasers.25 However, because the wavelengths of the lasers were fixed, the applicability of this method is limited. It is only suitable for the spherical gold nanoparticles (AuNPs). In this study, a tunable multiband laser was used for sample illumination to achieve the more flexible color modulation. A supercontinuum (SC) white laser provided the source of light. The SC laser produced a highly intense and collimated light with much shorter coherence length, while their spectral and intensity composition output could still be readily controlled like the conventional broadband lamps. To make the tunable multiband laser light, we designed a wavelength selector composed of four equilateral prisms and a mask. The first equilateral prism was used to disperse the incident light to a 1 cm wide chromatic dispersion light, and the second equilateral prism was used to generate the parallel chromatic dispersion light. After that, a movable thin mask with several narrow slits was introduced into the light path to select multiple narrow spectral bands from the broadband white laser and block others. We note that such a wavelength selection scheme would not be possible with tungsten light bulbs or LEDs, because their light output can hardly be collimated. Finally, after passing through the other two symmetrically placed equilateral prisms, the emergent light was converted back into an achromatic Gaussian laser beam for sample illumination. Since the width, the number and the relative position of the slits in the mask determined the bandwidth, the number and the peak positions of the source light, respectively, the spectral and intensity composition of the illumination light as well as the arrangements of the narrow slits should be designed according to the sample spectra for the best imaging effect. Considering that the scattering peak width of AuNRs utilized as the representative sample is about 40 nm, the bandwidth of the optimal illumination should be about 20 nm. In addition, because the LSPR scattering peaks of the AuNRs are generally distributed in the 580-700 nm region, the peak position of at least one of the narrow bands is preferred to be assigned in this range. To meet the above requirements, two different aluminum masks were designed of about 20 and 40 mm long, 10 mm wide and 2 mm thick with 12 and 18 individual slits of 0.50 to 0.75 mm wide and 8 mm long, respectively, wherein the distances between the used neighboring slits on mask A were unequal and on mask B were equal (Figure 1B and Figure S1). The spectral and intensity composition of the multiband laser light could be adjusted by moving the masks, which enabled the color modulation of the samples. The mask designs were verified by the simulation of the effects of color modulation as shown below. Simulation of Color Modulation. Compared to AuNPs with other shapes, such as gold nanospheres and triangular gold nanoplates,29, 30 anisotropic AuNRs are more sensitive to

the local environmental change and have been widely used for chemical and biological sensing.31-33 However, under the illumination of halogen-tungsten lamp, the red color of AuNRs was insensitive to the human eyes and hard to distinguish by the color image sensor, thus limiting its application in color analysis. On the basis of our understanding on the color formation mechanism, we found that the color modulation of AuNRs from the insensitive red region to the sensitive green region could be accomplished through the sharp multiband illumination. As a proof, the color images of four AuNRs with aspect ratios of 1.4, 1.6, 1.8, and 2.0 under the different lighting conditions resulted from two different masks (mask A with the unequal gaps and mask B with the equal gaps) were simulated. The results indicated that AuNRs could show different color appearances under the illumination of different laser multiband combinations (Figure 3). It can be seen that if certain one band of the source light is located at the right or red side of the LSPR spectra of AuNRs (mask A), as the mask moving to the left (the labeled peak shift from 687 nm to 650 nm), the integrated intensity value in the R channel would gradually increase, making the color of AuNRs gradually change from green to red (Figure 3A). If certain one band of the source light is located at the left or blue side of the LSPR spectra of AuNRs (mask B), as the mask moving to the left (the labeled peak shift from 578 nm to 562 nm), the integrated intensity value in the R channel would gradually reduce, making the AuNR color gradually change from red to green (Figure 3B). Since the green-to-red or red-to-green transition patterns vary for different AuNRs, by adjusting the multiband combination of the source light, different AuNRs could be easily identified.

Figure 3. Simulated color appearances of AuNRs with different aspect ratios by using the color camera under the illumination of different laser multibands through mask A or mask B adjustment.

Performance Evaluation of the Image System. Because the color appearances of samples are affected by many factors, such as the background, the exposure time, the quality of filter in CCD chip and the color conversion algorithm, the performance of the imaging system must be evaluated prior to the experiments. Herein, we investigated the influence of the background, which could be controlled by the system optimization, on the sample color. This was performed by simulating the u’, v’ values (the chromaticity coordinates in the perceptually more uniform CIE LUV colorspace) of the samples after different white noise background (with the maximum value up to one third of the sample signal) was added into the original images. The results showed as the background values in-


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Analytical Chemistry creased, the chromaticity of the samples all gradually shifted to the white balance region (Figure S2), denoting that the background could severely impact the color of the samples. Therefore, to avoid color distortion, the tunable multiband illumination device was combined with the previously reported light-sheet scattering imaging system which could provide the lowest background.34 From the viewpoint of the sample, the incident laser light was composed of several discrete wavelength bands after passing through the wavelength selector. Hence, spatially uniform combination of the laser multibands directly decided the imaging effect and the authenticity of the sample color. To evaluate the homogeneity of the source light in the illumination area, the R/G values of the background was statistically analyzed. Because the background of the imaging system was very low and the background values of many pixels were close to 0, each area of 20 pixels was used as one calculation unit. About 70-80 calculation units were selected from the image randomly. The statistical result showed that the R/G values of the background had a sharp distribution centered around 1.3 with only a slight fluctuation of 0.092 (Figure S3), denoting that the nonuniformity of the optical field could be ignored and the sample color acquired by this imaging system was authentic. Identification of AuNRs. To prove the feasibility of our color modulation strategy, we conducted a proof-of-concept experiment by utilizing the AuNRs with a maximum LSPR peak of 620 nm (aspect ratio of ~1.8) as the sample. The experimental results were consistent with the simulation results (Figure S4 and Figure S5), indicating that the continuous color modulation was successfully achieved through our strategy. Since different samples would generate different responses to the change of the source light, by using several combinations of the laser multibands, the visual sample identification could be realized by color analysis. Herein, two kinds of AuNRs with LSPR maximum at 625 nm and 650 nm (AuNR625 nm and AuNR650 nm), respectively, were used for demonstration. The results showed

Figure 4. The color images of the two kinds of AuNRs (AuNRs625 nm and AuNRs650 nm) under different laser multiband illuminations through mask B adjustment. The insert are the enlarged images of single AuNRs. Exposure time 20 ms. Scale bar 5 µm.

that with unidirectional movement of the mask and under continuous variation of the multiband illumination, the trends of the color change of the single nanoparticles were not the same (Figure 4). As mask B moving to the left, the color of the two kinds of AuNRs turned from red to green in different ways: when the labeled peak wavelength was located in 572 nm, AuNRs625 nm was more yellowish than AuNRs650 nm, and when the labeled peak was located in 565 nm, the color of AuNRs625

was green while the color of AuNRs650 nm was orange-red. Owing to the inherent heterogeneity of the chemically synthesized AuNRs and the variation of their orientation angles due to the spatial stochastic distribution,26, 28, 35 the color appearance of the particles in the images were not uniform. However, it had no effect on the identification results, because the overall color evolution processes of AuNRs625 nm and AuNRs650 nm with the sliding of the laser multiband mask were quite different, allowing the two kinds of AuNRs to be distinguished easily. This method could be further utilized to examine the spectral heterogeneity of chemically synthesized nanoparticles. Regardless of the apparent color of the particles under broad band light, when using different laser multibands for illumination, if the particles had similar spectra, their color variation trends should be about the same, but if the particles had differences in spectra, their color variation trends would also be different. That would enable the visual sample identification and classification. For example, in Figure S6, as the mask moving to the left, the color of the two AuNRs both changed from red to green (Figure S6A). We note that in the twodimensional R/G plot, there was only a little difference in the color change trends (Figure S6B). However, if the RGB data of the two AuNRs was visualized in a three-dimensional plot, the differences in color change trends was completely exhibited (Figure S6C). That means the combination of RGB data visualization and the laser multiband color modulation could provide more information, and enhance the species identification and differentiation capability of the imaging system. Detection of Hydrogen Sulfide. To illustrate the practicability of our strategy, we applied it to the detection of hydrogen sulfide based on Ag@AuNR, which is of great significance since hydrogen sulfide is not only an important signaling molecule in biological systems, but also a major air pollutant.36, 37 It is known that in the presence of oxygen, sulphide ions could react with Ag atoms and generate Ag2S at room temperature.38 Because the refractive index of Ag2S is significantly higher than Ag,39 the formation of Ag2S on Ag@AuNR surface would result in its LSPR maximum shifting to a longer wavelength. In the previous work of our group, Xiong developed a highly sensitive sulfide mapping method in live cells via multi-particle spectral imaging by using Ag@AuNRs as the nanoprobe,40 and the detection limit reached 0.01 nM. However, since the color of Ag@AuNRs is located in the insensitive red region and out of the applicable range of regular color analysis, a single particle spectral imaging device along with inefficient image processing steps was required. To bypass the complicated procedure, Hao developed a high throughput H2S sensing scheme by RGB colorimetric analysis, wherein the Ag@Au core-shell spherical nanoparticles, exhibiting green to orange color transition during the reaction, were used to replace the Ag@AuNRs as the nanoprobe.17 Nevertheless, since the spherical Ag@Au nanoparticles are less sensitive than the rod-shaped ones to the variation of their surrounding dielectric constant, the detection limit to H2S became much higher (~50 nM). nm



Figure 5. The color and spectral change of Ag@AuNRs during the reaction with NaHS. The dark-field images of Ag@AuNRs reacting with 1 µM NaHS aqueous solution at 0 (A) and 5 (B) min under the conventional halogen-tungsten lamp illumination. The transmission grating-based dark-field spectral images of Ag@AuNRs reacting at 0 (C) and 5 (D) min, where the inserts were the scattering spectral image of a single Ag@AuNR. (E) The fitted scattering spectra of the labeled AuNR. (F) The UV spectra of 1 mL Ag@AuNRs620nm diluted solution mixed with 100 µL of 10 µM NaHS aqueous solution in 0, 1, 3 and 5 min, respectively. (G) The time-dependent dark-field images of Ag@AuNRs during the reaction with 100 nM NaHS aqueous solution in 0-30 min under the fixed laser multiband illumination. Scale bar: 20 µm.

(Figure 5C-E), and the UV-vis spectra of a 1 mL diluted Ag@AuNRs solution before and after the mixture of 100 µL of 10 µM NaHS solution also showed a large redshift (Figure 5F). On the other hand, under the illumination of the laser multibands, the Ag@AuNRs showed an obvious color change during the reaction. After 100 nM NaHS aqueous solution was added into the flow cell, the spectra of Ag@AuNRs experienced a redshift from the fixed multibands, leading to the decrease of the integrated intensity value in the R channel of the camera, and the color of single Ag@AuNRs gradually turned from red to green (Figure 5G). For quantitative evaluation, Ag@AuNRs reacted with Na2S solutions of different concentration ranging from 0.1 – 100 nM were drop-casted onto coverslips and were examined under the microscope, respectively. As shown in Figure 6A and S8, there was a clear color difference as well as the distribution of the R/G values of the single nanoprobes between the 0.1 nM sample and the control blank under the multiband laser illumination, and the center of the distribution decreased gradually as the sulfide concentration increased from 0.1 nM to 100 nM. We note that each R/G value histogram was obtained by analysis of just one color image acquired within

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