# Digital microfluidic platform assembled into a home-made studio for sample preparation and colorimetric sensing of S-nitrosocysteine

Danielly Rocha, Richard P.S. de Campos, Habdias Silva-Neto, Gerson

Duarte-Junior, Fethi Bedioui, Wendell K.T. Coltro

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► To cite this version:

Danielly Rocha, Richard P.S. de Campos, Habdias Silva-Neto, Gerson Duarte-Junior, Fethi Bedioui, et al.. Digital microfluidic platform assembled into a home-made studio for sample preparation and colorimetric sensing of S-nitrosocysteine. Analytica Chimica Acta, 2023, 1254, pp.341077. 10.1016/j.aca.2023.341077. hal-04299093

HAL Id: hal-04299093

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Submitted on 23 Nov 2023

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Digital microfluidics platform attached on a home-made studio and
UV chamber for sample preparation and colorimetric sensing of S-
nitrosocysteine

Danielly S. Rocha,ª Richard P. S. de Campos,ᵇ Habdias A. Silva-Neto,ª Gerson F. Duarte-Junior,ª

Fethi Bedioui,ᶜ and Wendell K. T. Coltro *ª,d

*Corresponding Author:

Professor Wendell K. T. Coltro

Instituto de Química, Universidade Federal de Goiás

Campus Samambaia, 74690-900

Goiânia, GO, Brazil

Fax: +55 62 3521 1127

E-mail: wendell@ufg.br

ORCID: http://orcid.org/0000-0002-4009-2291

<footnote>ªInstituto de Química, Universidade Federal de Goiás, 74690-900, Goiânia, GO, Brazil</footnote>

<footnote>ᵇNanotechnology Research Centre, National Research Council of Canada, Edmonton, AB, Canada</footnote>

<footnote>ᶜInstitute of Chemistry for Life and Health Sciences i-CLeHS, Chimie ParisTech-PSL/CNRS 8060, Paris, France</footnote>

<footnote>dInstituto Nacional de Ciência e Tecnologia de Bioanalítica, Campinas, SP, Brazil</footnote>
**Abstract**

Digital microfluidics (DMF) is a versatile lab-on-a-chip platform that allows integration with
several types of sensors and detection techniques, including colorimetric sensors. Here, we propose,
for the first time, a fully integration of DMF chips into a mini studio containing a 3D-printed holder
with previously fixed UV-LEDs to promote sample degradation on the chip surface prior to a
complete analytical procedure involving reagent mixture, colorimetric reaction and detection
through a webcam integrated on the equipment. As proof-of-concept, the feasibility of the
integrated system was successfully through the indirect analysis of S-nitrosocysteine (CySNO) in
biological samples. For this purpose, UV-LEDs were explored to perform the photolytic cleavage of
CySNO, thus generating nitrite and subproducts directly on DMF chip. Nitrite was then
colorimetrically detected based on modified Griess reaction, in which reagents were prepared
through the programable movement of droplets on DMF devices. The assembling and the
experimental parameters were optimized, and the proposed integration exhibited satisfactory
correlation with the results acquired using a desktop scanner. Under the optimal experimental
conditions, the CySNO degradation into nitrite was 96%. Considering the analytical parameters, the
obtained linear range and limit of detection of CySNO were 12.5 to 400 µmol L⁻¹ and 2.8 µmol L⁻¹,
respectively. Synthetic serum and human plasma samples were successfully analyzed, and the
achieved results did not statistically differ from the data recorded by spectrophotometry at
confidence level of 95%, thus indicating the huge potential of the integration between DMF and
mini studio to promote complete analysis of low molecular weight compounds.

**Keywords:** luminosity control, droplet movement, on-chip decomposition, colorimetric sensor, S-nitrosothiols.

# 1. INTRODUCTION

In recent years, many technological efforts have been paid to simplify and automate the liquid-
handling process aiming integrating multiple analytical steps on single chip-based platforms to
achieve high-throughput microfluidic systems with sample-in-answer-out capability [1-5]. However, conventional microfluidic devices often rely on external equipment such as micro-pumps or high voltage power supplies to promote precise handling of fluids on microscale, which can be challenging depending on the demand of experiments [1,4,6-9].

In this scenario, digital microfluidics (DMF) have emerged as powerful platforms for promoting the automated and programable liquid handling in well-defined droplets based on application of controlled potential to an array of actuating electrodes [2,10-12]. Due to its versatility, DMF technology offers reconfigurability of experiments and automation integrated on a same platform [10,13], allowing performing multiple analytical procedures in a sequential mode including droplet sampling, mixing, splitting, and merging, thus simplifying the day-to-day laboratory routine [14,15], and becoming highly attractive for applications in bioanalysis and point-of-care testing [13,16,17].

As highlighted in the literature, DMF platforms can be embedded with different detection methods [3], providing potentiality for multi-assay approaches and reproducible routine analysis, thereby reducing operational user mistakes. Additionally, some inherent advantages of DMF are the use of low quantities of samples and reagents, portability, and design versatility. The DMF technology is compatible for integration with a variety of detection methods such as mass spectrometry [18], nuclear magnetic resonance [19], electrochemical detection [15], spectroscopic techniques [20-22] digital cameras [23,24] or smartphones [22,25,26].

Vo et al. [23] reported an interesting application of a DMF system combined with colorimetric detector and a white-colored box coupled with dual-branch fiber optic illuminator as an affordable analytical tool for monitoring droplet position and enzymatic colorimetric reaction. The authors showed that the assembling of camera and a zoom lens improved the colorimetric performance of enzymatic assay of β-glycosidase. In addition, it was used an image-based algorithm to analyze the enzymatic product to successfully perform the treatment and analysis of colorimetric responses. Li
et al. [21] proposed a DMF system with intelligent droplet tracking for estimating the droplet velocity on colorimetric assays using Griess reaction, as model. In the mentioned report, a correlation filter tracker was used to avoid interferences provided by background patterns or inhomogeneous illumination.

When associated to colorimetric responses, portable DMF systems are quite promising to enable the most varied chemical and biochemical analysis. In this context, promoting luminosity control and homogenous background light is essential for achieving high performance optical detection [21]. **Since** some models of DMF chips are commonly based on conductive materials that present color[22] or high reflection [23], the development of studies aiming to mitigate these challenges regarding colorimetric detection directly on DMF chips are extremely important in the realm of colorimetry. In addition, the development and integration of multiple analytical procedures from sample preparation to detection stages to allow a complete analysis using DMF systems can provide a powerful and portable tool for monitoring chemical species in real time [16].

Therefore, this study proposes, for the first time, the integration of a DMF platform with a mini studio to promote a semi-automated analytical protocol towards on-chip complete analysis, i.e., from sample preparation to colorimetric detection. As proof-of-concept, the conventional colorimetric reaction involving **modified Griess reagent** was explored for the indirect analysis of S-nitrosocysteine (CySNO), which was decomposed through exposure to UV light using blue LEDs. For this purpose, a 3D printed support combined with UV-LED was manufactured to enable on-chip biomolecule decomposition. The portable instrumentation was used **in-situ** for the full sample procedures, including decomposition, reagent mixing, droplet splitting and colorimetric analysis. A mini studio was manufactured using low-cost materials and then assembled with a webcam to simplify two important processes: background lighting and pixel capture. Lastly, it is important to emphasize that CySNO is a low-molecular weight S-nitrosothiol (LMW-RSNO), carrier of nitric oxide (NO), that present important physiological contributions in the human body. In this way, the
concentration of RSNOs can be determined to monitor numerous diseases, such as Parkinson's, Alzheimer's, sepsis, heart failure and asthma [27–30].

## 2. MATERIALS AND METHODS

### 2.1. *Chemicals and materials*

Sodium nitrite, monobasic sodium phosphate, dibasic sodium phosphate, ethylenediaminetetraacetic acid (EDTA), acetonitrile, sulfanilamide, N-(1-naphthyl)ethylenediamine dihydrochloride (NED), L-cysteine and hydrochloric acid, ethylenediamine tetrakis (ethoxylate-block-propoxylate) tetrol (Tetronic 90R4), tween 20, tween 80, triton X-100, and sodium dodecyl sulfate (SDS) were of analytical grade and used as purchased from Sigma Aldrich (Saint Louis, MO, USA). The ultrapure water used for solution preparation was processed through a water purification system (Direct-Q®3, Millipore, Darmstadt, Germany) with resistivity equal to 18.2 MΩ cm at 25 °C. Synthetic samples of normal and control serum were acquired from Doles (Goiânia, GO, Brazil) and Biotécnica (Varginha, MG, Brazil), respectively. Two blood plasma samples were received from the Laboratory of Clinical Analysis and Teaching in Health (LACES) from the Federal University of Goias.

Office paper (A4 size sheet) and adhesive tape (model 3M) were supplied by a local office supply store (Goiânia, GO, Brazil). The light emitting diode (LED) panel (dimension: 30 × 30 × 3.5 cm; 25 W), UV-LEDs and the printed circuit board were purchased from Embralumi (São Paulo, SP, Brazil) and Chipsce (São Paulo, SP, Brazil), respectively. The hollow cubic structure (dimensions: 25.5 cm (height) × 28.2 cm (length) × 27.0 cm (width)) composed by medium density fiberboard (MDF) and acrylonitrile butadiene styrene (ABS) thermoplastic filament (ø = 1.75 mm) were obtained from a local woodworking shop (Goiânia, GO, Brazil) and 3D Fila (Belo Horizonte, MG, Brazil), respectively.
## 2.2. Instrumentation

The 3D printer model Original Prusa i3 mk2/S, desktop scanner model G4050 and refrigerated micro centrifuge model NT 805 were purchased from Prusa Research (Prague, Czech Republic), Hewlett-Packard (Palo Alto, CA, United States) and Novatecnica (Piracicaba, SP, Brazil), respectively.

## 2.3. Fabrication of the mini studio

The mini studio was fabricated combining the following materials: MDF, office paper sheet and acrylic sheet. The MDF was utilized as the main structure of the holder. Four sheets of paper were fixed on each side and one acrylic sheet fixed on top of mini studio. Additionally, a LED panel was positioned on top of the mini studio at distance of *ca.* 13.5 cm. The assembling schematic representation of the mini studio segments is shown in Fig. S1 (Electronic Supplementary Material). This system was combined with a Logitech 920 webcam (DropBot component) to capture the images of chip (Fig. S2).

## 2.4. Device Operation

The digital microfluidic operations were performed using the DropBot system model DB3-120 equipped with 90-pin array, high reflective chrome electrodes and a Logitech 920 webcam from SciBots (Kitchener, ON, Canada). The DMF devices comprised of 90 independent droplet actuation electrodes were connected via pogo pins to the open source DropBot control system. The movement of the droplets was controlled by MicroDrop software (version 3.1.3) and assays were performed using either real-time actuation mode or with semi-automated protocol. Droplet driving potentials ranged from 90 and 120 V, frequency of 10 kHz and time intervals ranging from 1 to 5 s. For loading and movement of droplets, the standard solution and biological samples were prepared in
0.5 mmol L⁻¹ EDTA supplemented with 0.20% Tetronic 90R4 (v/v). The reservoirs were loaded with an aliquot (9 µL) of working solution and the droplets dispensed by activating a set of electrodes subsequently to the reservoir and deactivating the rectangular distribution electrode to generate a single unit droplet (~ 1 µL). The merging of two-unit droplets generated droplets of ~ 2 µL, as well as the division of one double-unit of droplet generated two single unit droplets. The droplet mixing process was performed by activating a circular layout of 4 electrodes mixed for ca. 30 s. The operation of dividing droplets from double to single unit was carried out by activating 3 sequential electrodes arranged linearly and later switching off the central electrode for 5 s. All DMF experiments were performed at room temperature (21 ± 2 °C).

## 2.5. Fabrication of 3D printed holder

For the decomposition of CySNO directly on the chip surface, a 3D printed holder was manufactured and integrated with UV-LEDs. The device was designed by using SolidWorks software and additive manufactured through fused deposition modeling (FDM) for ~ 30 min. For this purpose, the filament ABS (black color) was used as additive and the object printed under the following infill conditions: layer height (0.2 mm), density (20%), temperatures at 240 °C (nozzle) and 110 °C (bed). Next, the parallel-connected array of four LEDs was integrated into the 3D holder. Fig. S3 illustrates the procedures involving the manufacturing step of the device and its assembling onto DMF chip.

## 2.6. Synthesis and decomposition of S-nitrosothiols

The CySNO was synthesized from an adaptation of the method reported by Peterson and coauthors [31]. The synthesis was carried out in the presence of an equimolar mixture of 50 mmol L⁻¹ L-cysteine and nitrite (500 µL for each reagent) prepared in an aliquot of 2 mL of hydrochloric acid
(0.1 mol L⁻¹). After 5 min, the solution was diluted in 2 mL of a solution composed of 0.1 mol L⁻¹ PBS (pH = 7.4), prepared in 0.5 mmol L⁻¹ EDTA. The final concentration of CySNO was estimated by using UV-Vis spectrophotometry at 335 nm (ε = 736 M⁻¹ cm⁻¹). **For the decomposition of CySNO groups on the DMF chip, an aliquot of standard sample (9 µL) was positioned on the reservoir region and exposed to UV light for 15 minutes.**

## 2.7. On-chip colorimetric procedure

Two single unit droplets from different solutions, 50 mmol L⁻¹ sulfanilamide prepared in 0.02% (v/v) Tetronic 90R4 and 4 mmol L⁻¹ NED prepared in 0.01% (v/v) Tetronic 90R4 were dispensed from DMF reservoir electrode. Then, the droplets were merged, mixed and split into other new single unit droplets of **modified Griess reagent**. Next, the droplet containing the Griess reagent was merged and mixed with a single unit droplet containing the solution of nitrite or decomposed CySNO (Figs. 1A-C). For evaluating the impact of polymeric surfactants on colorimetric responses, the type of polymeric surfactants and their concentration were optimized by using Griess reaction on circular plastic spots. On-chip parameters optimization was performed by two different experiments: (i) mixing configurations (including circular and linear procedures) and (ii) mixing time.

Analytical curves for nitrite and CySNO were done by using on-chip sequential dilutions from a 400 µmol L⁻¹ stock concentration (either for nitrite or CySNO species). More information can be visualized in the Video S1 (available as supplemental). The repeatability and reproducibility of the semi-automatic protocol dedicated to the sample preparation, colorimetric reaction as well as the detection were successfully investigated based on the combination of real-time mode and pre-programmed operations.

## 2.8. Colorimetric detection
The on-chip colorimetric detection was monitored via image capturing with a high-resolution webcam (1280 x 720 pixels, frame rate = 10 FPS). The colorimetric analysis was performed in two simple steps: (i) the region of interest from colorimetric zones was captured by a print screen command and converted as PNG file format; (ii) the image colorimetric zone was analyzed by using Corel® PHOTO-PAINT™ X7 software, converting the color scale CMYK to magenta channel and collecting the pixel intensity (Fig. 1D). The pixel analysis used is like to other protocols reported in literature that successfully combine colorimetric paper-based devices with desktop scanner [32,33].

A schematic representation showing the chip design, reservoir sections, distribution of electrodes, actuation electrodes, colorimetric detection zones and the color capture process on zones are illustrated in Fig. S4.
<figure><img src="image_3.png" /><figcaption>**Fig. 1.** (A) DMF and mini studio components. (B) Integration between 3D printed UV camera and DMF platform. (C) Droplet generation, fluid arrangement system and decomposition of CySNO by UV radiation and mixture of the degradation product with the chromogen (modified Griess reagent) and image capture by print screen command and subsequent acquisition of pixel intensity using graphic design software.</figcaption></figure>

## 2.9. *Sample preparation*

Two synthetic serum samples and two plasma samples were utilized as model biological matrices. The above-mentioned samples were prepared by adding acetonitrile solution (dilution: 1:1 (v/v)) and centrifuging at 12000 rpm for 5 min. Next, the supernatant sample was diluted in a solution
composed by 0.1 mol L⁻¹ PBS (pH 7.4), 0.5 mmol L⁻¹ EDTA and 0.2% Tetronic 90R4 (supernatant dilution: 1:1 (v/v)).

## 2.10. On-chip and reference analysis

For the comparative analysis, two different biological components were utilized: (i) synthetic blood serum and (ii) plasma samples. The experiment was done in the absence and presence of sample spiked with three different concentration levels of CySNO (25, 175 and 325 µmol L⁻¹). For the DMF experiment, synthetic blood serum and plasma samples were loaded onto the chip reservoir. Next, samples were decomposed under UV light, dispensed, and mixed with **modified Griess reagent**, and image was captured on device for CySNO **analysis of pixel intensity**. All DMF colorimetric assays were performed in quadruplicate.

In parallel experiments, the UV-Vis spectrophotometer model UV-M51 was purchased from BEL PHOTONICS (Piracicaba, SP, Brazil) and a calibration curve of nitrite was generated based on Griess-method (Fig. S5) [34,35]. Prior to spectrophotometric analysis, samples were submitted to the decomposition step. Next, the indirect detection of CySNO in the samples was achieved by combining aliquots of 1.5 mL of sample with 1.5 mL of Griess reagent in the quartz cuvette. Spectrophotometer responses were recorded at 510 nm (n=3).

## 2.11. Characterization

The performance of mini studio was characterized by four studies. (i) The background light responses from chip before and after the utilization of mini studio; (ii) the analysis of illuminance of mini studio, (iii) the comparison between the pixel intensity obtained on mini studio-webcam and desktop scanner responses and, (iv) the evaluation of the assembling of mini studio and DMF system yielded reproducible light quality results.
Background light results were collected with a print screen command of the chip in the absence and presence of mini studio. The analysis of luminosity was performed before starting the colorimetric analysis by using the smartphone application Lux Light Meter [36,37]. This step was done by positioning the smartphone between the LED panel and the top of mini studio.

The comparative study involving the pixel capture was performed by collecting the pixel intensity directly on mini studio through a print screen command and by capturing the colorimetric zones through desktop scanner procedure [38,39]. These studies were used the Griess assay on polyester microwell plate [40] as a model colorimetric reaction. Repeatability and reproducibility were calculated considering three sequential assembling stages of mini studio and analyzing the background signals directly collected on DMF chip.

# 3. RESULTS AND DISCUSSION

## 3.1. *Integration of colorimetric detector on chip*

DMF has been successfully explored in numerous cases involving chemical and bioanalytical applications due to its benefits as a programmable droplet manipulation tool, full sample preparation capabilities, portability, and potential to be integrated with different detection approaches [2,10,14]. However, the most popular examples reported in the literature do not explore the colorimetric analysis without laborious mathematics pre-treatment. In addition, the control of background lighting induced auto-reflection patterns on the metallic surfaces of the DMF electrodes can be challenger for reproducible optical analysis [20,22,25,26,41,42].

In view of the recent studies, the strategy to mitigate the inconveniences can be solved through manufacturing an alternative mini studio and combining the luminosity control approach inside the studio with the subsequent capture of the pixel responses from colorimetric product formed directly on a chip using a basic print screen command.
## 3.2. Fabrication and characterization of mini studio

The proposed strategy exploiting a mini studio was evaluated to control the background light on the DMF chip during the colorimetric experiments. A luminosity control tool was constructed based on combination of four “do it yourself” materials. First, a cubic structure composed by MDF containing open faces was designed. The side faces of the MDF structure were assembled with three sheets of paper, while the top face of the cube housed one sheet of white acrylic. Finally, a LED panel was successfully positioned at 13.5 cm from the top of mini studio. We decided to insert the acrylic structure on top of mini studio to ensure homogenous light diffusion from the LED panel.

To examine the performance of the mini studio, the DMF system was positioned inside of proposed tool and three different experiments were carefully evaluated. One study was carried out by collecting the background light responses from the chip in the absence of mini studio and through the usage of a mini studio with five different LED configurations. As indicated in Fig. 2, the studied modes include the use of (i) Luminaires with 36 LEDs, (ii) 20 circular LEDs inside the mini studio, (iii) 60 LEDs, (iv) 30 LEDs and (v) panel LED. The choice of the most promising strategy for lighting control was selected based on the pixel intensity values of the 30 central actuation electrodes.

The second experiment was based on illuminance measurements collected between the LED panel and top of mini studio (Table S1). Finally, the third characterization was performed comparing the colorimetric responses on proposed system with results obtained by digitalization on desktop scanner (Figs. S6 A-C). The desktop scanner colorimetric experiments were performed on a polyester microwell plate [40] since the chrome DMF chip could not be scanned to be used as a comparison parameter due to its high reflection.
<figure><img src="image_4.png" /><figcaption>**Fig. 2.** Strategies for luminosity control on the DMF chip. (A) Absence of studio. (B) Use of “do-it-yourself” mini studio. (C) Mini studio associated with 20 internal LEDs. (D) Mini studio associated with a circular array of 36 LEDs. (E) Mini studio coupled to 60 rectangular LEDs. (F) Mini studio coupled to a rectangular array of 30 LEDs. (G) Mini studio combined with ceiling light. (H) Histogram of the average pixel intensities of the different lighting strategies (n = 30).</figcaption></figure>

The results seen in Fig. 2 represent the luminosity on the surface of the DMF chip before and after the utilization of mini studio and their background light results obtained through magenta color channel. The pixel responses observed in the absence of mini studio indicated poor homogeneity of background signal, with pixel intensity value of 102 ± 52 AU. This behavior can be associated with the reflection of electrodes present on the DMF chip (see Fig. 2A). As indicated in Figs. 2B-F, the presence of mini studio integrated with different LEDs revealed gradual reduction of background light and improvement of pixel homogeneity, with pixel intensity responses ranged between 63 ± 11 and 3.1 ± 0.3. The best background light condition was observed when the mini studio was
integrated with a LED panel, which demonstrated a decrease in the background signal in terms of pixel intensity in ~ 97% when compared to the background signal in the absence of mini studio (Fig. 2G). Further information related to the proposed mini studio can be seen in Fig. S2.

It is important to mention that the proposed approach combining the mini studio, LED panel and webcam can promote acceptable background light control, thus emerging as affordable alternative for monitoring colorimetric reactions on-chip. In addition, the adapted optical sensor is an auxiliary component from the DropBot system that allows a direct connection to the MicroDrop software, making possible any adjustments involving the image capture process.

Table S1 shows the obtained values of light intensity density with the mini studio. The illuminance analysis indicated a light intensity density value of ~ 12689 ± 545 Lux (n = 3). This qualitative measurement exhibited reproducible results, indicating high homogeneity of light emission from the LED panel. Thus, the simple combination of DMF system and the mini studio can solve two important challenges in optical sensing: strong variations in the background signal from chip and erroneous interpretations of the color responses extracted from the colorimetric assays due to background variation. In addition, the proposed system has ensured great simplicity in the collection of the analytical signal and dispenses additional data treatments.

Figs. S6A-B demonstrate the colorimetric responses obtained from desktop scanner in comparison to the mini studio integrated with webcam. Based on each analytical curve, the sensitivity values achieved using scanner and mini studio were 0.252 and 0.215 AU/µmol L⁻¹, respectively. The better sensitivity performance observed upon the desktop scanner can be associated to the fact that it presents a robust control of luminosity. However, the required time to digitalization the colorimetric zones via desktop scanner was ~ 5 min. On the other hand, the mini studio assembled with webcam provided similar background light noise and faster digital image acquisition (~ 5 s). The statistical correlation between the responses obtained with the mini studio system versus the desktop scanner method was also evaluated (Fig. S6C). The calculated correlation value was 0.9, which suggests
high correlation between both methods. Thus, the proposed mini studio (when combined with
webcam tool) can promote a favorable and simple process for digitalization the colorimetric zones
on-chip. Considering the faster time for performing all colorimetric assay with the print screen
function acquisition, the proposed tool has great potential for monitoring the kinetic colorimetric
reactions.

To evaluate the repeatability and reproducibility of the light control conditions when assembling the
mini studio and DMF system together, three procedures in sequence were performed. The chip
surface images were acquired after each assembly and the relative standard deviation (RSD) values
from background light noise of the 30 central actuation electrodes were calculated for each
experiment. The obtained results of pixel intensity were submitted to one-way analysis of variance
(ANOVA; p = 0.05). The results summarizing the captured zones and their respective histograms
are shown in Figs. S7A-D. The calculated values of RSD for repeatability and reproducibility
ranged from 4 and 2%, respectively, thus indicating satisfactory performance. Furthermore, through
the ANOVA analysis, a F test was performed, and the obtained value (F = 2.14) was lower than the
critical F value (Fcrit 3.10), indicating absence of statistical difference between the three different
assembling processes at 95% confidence level. These results suggest that the procedure of
colorimetric analysis can be performed in a large space of the chip, discarding the need to move the
colorimetric droplet to the specific point on the chip.

3.3. Optimization of operational parameters

The utilization of surfactants in DMF assays facilitates the actuation of droplets, improving the
sampling and reducing possible biofouling on electrode surfaces [15,18,43,44]. To evaluate the
effects of surfactant on colorimetric Griess reaction responses, a set up experiments was carried out
in the absence and presence of following surfactants: Tetronic 90R4, triton-X 100, tween 20, tween
80 and sodium dodecyl sulfate (SDS). Colorimetric signal was captured between 0 and 30 min after
starting the Griess reaction. Additionally, the concentration of the selected above polymeric surfactant was optimized with prepared levels ranging from 0.001 to 0.2% (v/v).

As can be seen in Fig. S8, colorimetric zones in the absence and presence of surfactants did not show significant difference in terms of pixel intensity, which values ranged from 23 to 28 AU. This result indicates that the presence of surfactants does not alter significantly the signal intensity for the detection of nitrite. In addition, the utilization of Tetronic 90R4 as polymeric surfactant exhibited the best colorimetric response. The best chemical conditions for the Griess assay on-chip were obtained when the solutions of sulfanilamide, NED and nitrite were supplemented with Tetronic 90R4 at concentrations 0.02, 0.01 and 0.20% (v/v), respectively. Additional results are shown in Table S2.

The effect of different types of droplets mixing on-chip was also investigated through both circular and linear mixing configurations. A study reported by Li *et al.* [21] using the circular droplet mixing pattern was employed to assess the reaction kinetics. In their study, the authors used droplets of *ca.* 15 µL to assess the colorimetric response after incubation times between 1 and 10 minutes.

In this current report, optimizations for the droplet mixing type and time using the colorimetric Griess reaction were performed using a droplet of 200 µmol L⁻¹ NO₂⁻ in 0.1 mol L⁻¹ PBS (pH = 7.4), 0.5 mmol L⁻¹ EDTA and 0.20% Tetronic (v/v) and a droplet of prepared Griess reagent. Driving potential was 100 V and, mixing time ranged from 1 to 60 s, as summarized in Fig. 3. The observed behavior in both droplet mixing processes did not provide noticeable differences on colorimetric results, which calculated values of pixel intensity of 27.7 ± 0.4 and 27 ± 1 AU, for circular mixing and linear mixing strategies, respectively (see Figs. 3A-B). However, the mixture process via circular approach exhibited lower RSD values and, consequently, it was selected as ideal operation condition. The results displayed in Figs. 3C-D indicate that the time of 15 s is insufficient to promote a good diffusibility for mixing between NO₂⁻ and Griess reagents. On the other hand, the
best droplet mixing efficiency was acquired at ~ 30 s. It was also observed that for longer mixing procedures ($t > 60$ s), droplets may be susceptible to decrease in color intensity formed on-chip.

<figure><img src="image_5.png" /></figure>

<figure><img src="image_6.png" /></figure>

<figure><img src="image_7.png" /></figure>

<figure><img src="image_8.png" /></figure>

**Fig. 3.** Mixing type and time of mixing using 200 µmol L⁻¹ NO₂⁻ in 0.1 mol L⁻¹ PBS (pH = 7.4), 0.5 mmol L⁻¹ EDTA and 0.20% Tetronic (v/v) after reaction with Griess reagent. (A) Magenta color product of the reaction in the colorimetric detection zones on the DMF chip after assays utilizing circular and linear mixing. (B) Histogram of pixel intensity obtained in the automated mixing type optimization test (n = 4). (C) Magenta reaction product in the colorimetric detection zone after different times of automated mixing. (D) Histogram of pixel intensities obtained at different mixing times (n = 4).

### 3.4. On-chip CySNO decomposition

The Saville method is one of the main approaches utilized in literature for the decomposition of S-nitrosothiols and their indirect colorimetric detection [45]. This procedure is based on the use of mercury(II) chloride as catalyst material to convert S-nitrosothiols groups in nitrite and other
subproducts. Subsequently, the nitrite can be measured by using the combination of Griess reaction and spectrophotometric techniques. Other studies successfully reported the use of toxic reagent free methods, such as UV radiation exposure, for promoting S-nitrosothiols degradation with similar results [46,47].

In this study, we propose the degradation of S-nitrosothiols directly on DMF chips using the reagent-free approach by combining a customized UV-3D printed holder coupled with DMF apparatus. The proposed plastic holder uses UV-LEDs mounted on a 3D printed support that can be positioned on top of the DMF device. Aiming for best performance, the on-chip degradation of CySNO group was optimized through five different experiments ranging the degradation time from 0 to 15 min. Fig. S9 illustrates the real image of system used UV-LED in combination with DMF platform. The observed percentage of CySNO decomposition into $NO_2^-$ through the homolytic cleavage via UV exposure was 7, 45, 54, 78, and 96% for times 0, 2.5, 5, 10 and 15 min, respectively. Thus, the degradation time of 15 min yielded the best condition for performing the reagent-free on-chip degradation process of CySNO groups (see Figs. 4A-B), which is in good agreement with previously reported studies [28,48]. In the above-mentioned condition, the humidity control in the environment is recommended to prevent evaporation processes, as described by other authors [49,50]. UV degradation times longer than 15 min should be avoided (for on chip experiments), deviation of the expected degradation results could happen as result of the droplet evaporation on device. An alternative to improve this effect could be the introduction of an immiscible fluid system through use of oil to prevent droplet evaporation [51,52].

### 3.5. Sensing performance

Two different analytical curves were constructed by loading the Griess and target analyte solutions on chip under optimized experimental conditions. One analytical plot was performed using standard solutions of nitrite while the second was obtained with CySNO. Both analytical curves were
acquired with standard solutions prepared in the concentration range from 12.5 to 400 µmol L⁻¹ (Fig. 4 C-D). The linear equations were y = (0.152 ± 0.004)x - (1.8 ± 0.3) with R² = 0.998 and y = (0.141 ± 0.003)x - (0.4 ± 0.4) with R² = 0.997 for the responses of NO₂⁻ and CySNO samples, respectively. The limit of detection (LOD) was calculated based on 3*SD<sub>blank</sub>/slope (n = 10). The calculated LOD values for both NO₂⁻ and CySNO were 2.8 µmol L⁻¹. As showed in Table S3, the analytical parameters exhibited similar performance when compared to other results reported in literature.

<figure><img src="image_9.png" /><figcaption>**Fig. 4.** (A) Colorimetric response of 400 µmol L⁻¹ CySNO prepared in 0.1 mol L⁻¹ PBS (pH = 7.4), 0.5 mmol L⁻¹ EDTA and 0.20% Tetronic 90R4 (w/v) before and after degradation in different times on the DMF device. (B) Pixel intensity as a function of CySNO degradation time (n = 4). (C) Concentration gradient obtained in the construction of the analytical curve of NO₂⁻ and CySNO on the digital microfluidics platform. (D) Analytical curve obtained for NO₂⁻ and CySNO (after degradation with UV light) in 0.1 mol L⁻¹ PBS, 0.5 mmol L⁻¹ EDTA and 0.20% Tetronic (v/v) using DMF (n = 4).</figcaption></figure>
The repeatability and reproducibility of the semi-automatic protocol on chip involving the detection of NO₂⁻ and CySNO were calculated based on pixel intensity responses (one device with n = 4 for repeatability; two devices with n = 8 for reproducibility). In terms of repeatability, the respective RSD values involving the detection of NO₂⁻ and CySNO were 2 and 4%, respectively. Considering the reproducibility study, the found values ranged from 5 and 8%. It is important to mention that, to the best of our knowledge, this is the first report that uses the combination of mini studio and DMF system for *in-situ* degradation and detection of CySNO on-chip.

### 3.6. Practical applicability and reference analysis

As proof of concept, the proposed semi-automatic method was selected for the UV-decomposition, reagent preparation and indirect detection of CySNO in four biological samples (two control serum and other two blood plasma samples). Aiming to validate the proposed method, two distinct accuracy approaches were carefully explored by using synthetic and blood serum samples: recovery study and by using a reference analysis (spectrophotometric technique). The biological solutions were spiked with CySNO standard solutions prepared at three concentration levels: 25, 175 and 325 µmol L⁻¹. Table 1 summarizes the obtained analytical responses using the DMF and spectrophotometry.

<table><caption>**Table 1** Comparison between three level CySNO addition and recovery assay for artificial serum and blood plasma samples using DMF and spectrophotometry techniques.</caption><thead><tr><th rowspan="2">Sample</th><th rowspan="2">Spiking level</th><th rowspan="2">Added (µmol L<sup>-1</sup>)</th><th colspan="2">DMF</th><th colspan="2">Spectrophotometry</th></tr><tr><th>(µmol L<sup>-1</sup>)</th><th>Recovery (%)</th><th>(µmol L<sup>-1</sup>)</th><th>Recovery (%)</th></tr></thead><tbody><tr><td>Serum</td><td>#1</td><td>25</td><td>26 ± 4</td><td>104</td><td>21 ± 1</td><td>84</td></tr><tr><td>Serum</td><td>#2</td><td>175</td><td>148 ± 4</td><td>84</td><td>160 ± 1</td><td>91</td></tr><tr><td>Serum</td><td>#3</td><td>325</td><td>255 ± 4</td><td>78</td><td>278 ± 6</td><td>86</td></tr><tr><td>Plasma</td><td>#4</td><td>25</td><td>25 ± 9</td><td>100</td><td>25 ± 1</td><td>100</td></tr><tr><td>Plasma</td><td>#5</td><td>175</td><td>140 ± 9</td><td>80</td><td>149 ± 9</td><td>85</td></tr><tr><td>Plasma</td><td>#6</td><td>325</td><td>297 ± 18</td><td>91</td><td>255 ± 7</td><td>78</td></tr></tbody></table>
As shown in Table 1, the recovery values for DMF tests ranged from 78 to 104% and 80 to 100% for the control serum and blood plasma samples, respectively. In addition, the results presented in Table 1 demonstrate that analysis of CySNO through DMF and reference technique exhibited similar performance. Considering a statistical analysis (t-test), the respective t values (0.99 and 1.15) for the data recorded using serum and plasma as samples were lower than the critical t value (2.91), thus indicating that the results did not differ significantly at a 95% confidence level. Based on the achieved results, the proposed approach has provided satisfactory accuracy through a semi-automatic protocol for performing the colorimetric analysis. Finally, two control serum samples and two blood plasma samples were analyzed before and after degradation step. Fig. 5 shows the obtained images for the tested samples and the obtained nitrite concentrations before and after CySNO degradation are presented in Table 2.

<figure><img src="image_10.png" /><figcaption>**Fig. 5.** DMF images of control serum and blood plasma samples before and after degradation with subsequent reaction with Griess agent.</figcaption></figure>

<table><caption>**Table 2** NO₂⁻ and CySNO values found in control serum and blood plasma diluted samples.</caption><thead><tr><td>Sample</td><td>Nitrite (µmol L<sup>-1</sup>)</td><td>LMW-RSNO (µmol L<sup>-1</sup>)</td></tr></thead><tbody><tr><td>#1</td><td>18 ± 4</td><td>&lt; LOD</td></tr><tr><td>#2</td><td>23 ± 6</td><td>&lt; LOD</td></tr><tr><td>#3</td><td>20 ± 3</td><td>&lt; LOD</td></tr><tr><td>#4</td><td>27 ± 7</td><td>6 ± 1</td></tr></tbody></table>

As displayed in Table 2, the measured level of nitrite in biological samples (#1 to #4) ranged from 18 to 27 µmol L⁻¹ and the indirect analysis of CySNO in samples #4 was 6 ± 1 µmol L⁻¹. It is
important to mention that the nitrite response (pre irradiation) was subtracted from the RSNO
response (post irradiation). In addition, the concentration of CySNO was expressed in terms of low-
molecular weight S-nitrosothiol. Unfortunately, the levels of CySNO in samples #1, #2 and 3# were
below the LOD and their concentrations were not quantified. The assembling of DMF chips into a
mini studio containing a 3D printed holder with integrated LEDs has successfully demonstrated a
huge potential to promote a complete analysis including the on-chip degradation of the biological
compound, which can be useful for assessing quantitative information on the concentration of target
and clinically relevant compounds on biological fluids.

4. CONCLUSIONS

This study has described the assembling and integration of DMF platforms into a mini studio for
controlling the background light during colorimetric measurements, thus enhancing the ability of
digital microfluidics to perform reproducible complete analysis with sample-in-answer-out
capability. The strategy adopted herein comprised the integration of 3D printed holder containing
previously fixed UV LEDs to promote for the first time the degradation of low molecular weight S-
nitrosothiols directly on DMF chips prior to promote a complete analytical procedure through a
semi-automated protocol. The proposed approach offered great performance for real sample
analyzes with no statistical difference from the results recorded through a reference technique.

Lastly, it is important to highlight that the integration of UV degradation directly on DMF chip
followed by reagent mixing, colorimetric reaction and signal readout opens a new gate to be
explored in analytical and bioanalytical chemistry including, but not limited, the analysis of other
target compounds potentially relevant for clinical diagnostics.

CRediT authorship contribution statement
**Danielly S. Rocha:** Conceptualization; Methodology; Software; Validation; Formal Analysis; Investigation; Data Curation; Writing – Original Draft Preparation; Visualization. **Richard P. S. de Campos:** Conceptualization; Methodology; Investigation; Writing – Review & Editing; Visualization. **Habdias A. Silva-Neto:** Conceptualization; Methodology; Validation; Formal Analysis; Investigation; Data Curation; Writing – Review & Editing; Visualization. **Gerson F. Duarte-Junior:** Methodology. **Fethi Bedioui:** Conceptualization; Writing – Review & Editing.

**Wendell K. T. Coltro:** Conceptualization; Resources; Writing – Review & Editing; Supervision; Project Administration; Funding Acquisition.

**Declaration of competing interest**

The authors declare no competing financial interest.

**Acknowledgments**

The authors would like to thank CAPES (finance code 88887.192880/2018-00 and 001), CNPq (grants 307554/2020-1 and 405620/2021-7) and INCTBio (grant 465389/2014-7) for the financial support and granted scholarships. The Laboratory of Clinical Analysis and Health Education (LACES) and Professor Paulo Sérgio de Souza are also acknowledged for donating blood plasma samples for DMF analysis and technical supporting for spectrophotometric analysis, respectively. Lastly, the authors would like to thank Professor Aaron Wheeler for suggestions, discussions, and support to implement DMF in our group.

**Appendix A. Supplementary data**

The following is the Supplementary data to this article:

Experimental optimizations, characterizations, and real illustrations of the proposed system (PDF).

A semi-automatic analytical procedure video is also supplied in separate file (MP4).
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