Characterization of Metallic Gold Nanoparticles in a Colloidal State by Artificial Vision

Gold nanoparticles in their colloidal state have different colors, and the equipment for their characterization, such as UV-Vis spectrophotometers, scanning electron microscopy (SEM), and transmission electron microscopy (TEM), has high costs. The research aimed to characterize metallic gold nanoparticles by artificial vision based on the color of the samples in the colloidal state. The sensor used for the sampling was a 50 MP triple-lens camera with the optical image stabilization (OIS) of a smartphone. The Vision Acquisition and Vision Assistant blocks in the NI LabVIEW platform were used to implement an artificial vision device. The camera interface was used to identify the color of each of the 10 samples of colloidal gold nanoparticles produced by the YAG laser and chemical reduc - tion in 15 ml of deionized water. The characterization consisted of the determination of the size and concentration of the gold nanoparticles based on their color, which ranged from pink to red wine. As a result, the artificial vision device adequately identified the color of the metallic gold nanoparticles in a colloidal state with a certainty of more than 95%, allowing the nanoparticles to be adequately characterized. Therefore, it is concluded that artificial vision adequately characterized gold nanoparticles’ wavelength, absorbance, diameter, and concentration.


INTRODUCTION
Metal nanoparticles (NPs) are particles less than 100 nanometers in diameter that have unique properties that differentiate them from macroscopic-scale materials.These properties make them attractive for a wide range of applications, such as biomedicine (Paramasivam et al., 2024), electronics (Weichelt et al., 2019), catalysis, and optics such as in the treatment of contaminated water (Kumari et al., 2024).
Gold nanoparticles (AuNPs) are a type of metallic NP that have been widely studied due to their unique optical properties (Liu et al., 2021).AuNPs are highly dispersible in water and exhibit a characteristic red color variant according to their size and shape.This property has made them a promising material for applications in bioimaging, biosensing, and photodynamic therapy (Hua et al., 2021).
Characterization of AuNPs is critical for the development of successful applications.The properties of AuNPs, such as their size, shape, size distribution, and aggregation state, can significantly affect their performance in a given application.Traditionally, AuNPs have been characterized using laboratory techniques, such as optical spectroscopy, electron microscopy, and X-ray diffraction.These techniques are accurate but are laborious and expensive.
In recent years, artificial vision (AV), part of artificial intelligence, has become a tool that makes color, image, and morphology detection possible (Balarezo et al., 2022;Serafino et al., 2020).AV can automate image analysis (Montalvan et al., 2022) and provide quantitative and qualitative information about image properties.

ECOLOGICAL ENGINEERING & ENVIRONMENTAL TECHNOLOGY
LabVIEW is a software tool designed for the development of applications intended for monitoring, control, and design using graphical programming (Korgin et al., 2019); its distinctive feature lies in providing a user-friendly environment, by adopting a graphical approach, Lab-VIEW simplifies the creation of complex applications by replacing extensive lines of code with an intuitive visual environment (Sivaranjani et al., 2021).LabVIEW as a virtual instrument mimicking instrument simulation software can display real-time object detectors with IGUI design (Fawwaz et al., 2020).LabVIEW has modules developed for AV, among these modules are Vision Acquisition and Vision Assistant (NI, 2023); in addition, it has great compatibility with other programs, cameras, and vision systems.
Early applications of AV focused on object detection and counting, nowadays, AV is used to characterize a wide range of object properties, such as size, shape, size distribution, and aggregation states (Wisultschew et al., 2019).With AV, image analysis can be automated, which reduces the time and effort required for characterization; it can be adapted to different sample types and experimental conditions.It requires a significant amount of training data to achieve optimal performance (Arsalane et al., 2020).The AV method of image analysis uses image processing techniques to extract information about the properties of objects such as AuNPs found in a colloidal state, this method includes edge detection, image segmentation, and color analysis (Ganchovska & Krasteva, 2022).
The application of AV in the characterization of aqueous substances is achievable by color detection; as the study of Ordoñez-Avila et al. (2022), where they implemented controllers to detect color change by AV in the formation of ice in an evaporator, once the desired color is obtained, the controller sends a shutdown signal; with which obtained water of a defined color.On the other hand, nanoparticles have been the subject of continuous research and development.In the study by Nag et al. (2020), the capabilities of VA for size and shape determination of metal nanoparticles in colloidal solutions were explored; the results highlighted the effectiveness of this methodology in overcoming the challenges associated with size and morphology variability, thus laying the foundation for this research.
In this context, VA is an approach to overcome the cost and speed limitations in characterizing AuNPs in a colloidal state, by applying advanced image processing algorithms implemented in LabVIEW using Vision Acquisition and Vision Assistant (Issa et al., 2019).VA enables a quantitative and qualitative evaluation of the properties of NPs in colloidal solutions.This methodology is presented as an innovative tool for the rapid characterization of AuNPs in colloidal environments with color sorting.

Gold nanoparticle synthesis
The synthesis of AuNPs was carried out by the laser ablation method, a process that uses a laser, with which it was ablated on a solid gold plate of 10×15 mm with a thickness of 1 mm inside ultrapure deionized water.This method allowed the production of AuNPs dispersed in solution, with significant control over their size and characteristics.A high-energy pulsed Nd: YAG (Neodymium: Yttrium-Aluminum-Garnet) laser was used, emitting short 6 ns and powerful pulses up to 450 mJ/pulse for different wavelengths (Quantel, 2019).
The laser beam was focused on the Au solid plate, located 100 mm away from a convex lens (Figure 1).The laser energy is absorbed by the Au, which is immersed in water, generating plasma at this solid-liquid interface.The interaction between the laser and the gold target produces a hot, highly energetic plasma on the surface of the material.The plasma reaches extremely high temperatures (Torrisi et al., 2020), leading to vaporization and the formation of a plasma plume.The laser parameters, such as pulse energy and repetition rate, as well as the focal length and duration of the ablation process, were tuned to produce AuNPs with different characteristics.
The ablation of the Au target generates microscopic fragments and NPs in the plasma plume; the laser energy breaks the atomic bonds, generating ions and atoms that condense and form NPs during the expansion and cooling of the plasma.These generated Au are dispersed in ultrapure water and stabilized in the form of colloids with characteristic colors according to their size and concentration.Analyses are performed to characterize the properties of the AuNPs synthesized by the laser ablation method; such as size, shape, and concentration.
As shown in Figure 2, also, AuNPs are produced by the size-controlled chemical reduction method (Zhou et al., 2023); employing an aqueous solution of gold chloride (AuCl 4 ) as a precursor and a reducing agent, such as sodium borohydride (NaBH 4 ), under controlled conditions of temperature and agitation (Iqbal et al., 2016).
The formation of AuNPs by both laser ablation and chemical reduction methods was confirmed by UV-Vis spectroscopy using AvaSpec-ULS2048x64-EVO equipment (Avantes, 2018).

Colloidal solutions preparation
The colloidal solutions of the synthesized AuNPs were dispersed using an ultrasound (US) system at 40 kHz (Carbajal-Morán et al., 2022), to ensure a homogeneous distribution (Figure 3).The concentrations of the colloidal solutions were adjusted to encompass a suitable range of sizes and optical densities.
Once the AuNPs were homogenized in a colloidal state, the samples were placed in 5 ml transparent glass vials to be characterized by artificial vision.The Vision Acquisition block was configured to acquire the image of the AuNPs in video mode with a resolution of 29.97 fps (Figure 5). Figure 7 shows the selection of the control by classes with color classification of the AuNPs and indicators for access from LabVIEW.
From the classification and characterization diagram of the AuNPs colloid samples developed in LabVIEW (Figure 8), it was possible to plot the maximum peak absorbance as a function of its wavelength, by reading the corresponding *.csv file that was displayed on the Graph element of LabVIEW.(1) With "d" obtained in Eq. 1, the concentration of AuNPs was calculated, using Equation 2: where: A spr -represents the absorbance at peak plasmon resonance, C Au -concentration of nanoparticles mg/l, with experimental values C 1 = -4.750and C 2 = 0.314 (Haiss et al., 2007).
From the front panel of the artificial vision system developed in LabVIEW, presented in Figure 9, it was possible to characterize the samples of AuNPs in a colloidal state; showing the signal of the observance, the diameter, and the concentration of NPs, calculated with equations 1 and 2.

RESULTS AND DISCUSSION
With the artificial vision system implemented in LabVIEW, it was possible to characterize the AuNPs that were in the colloidal state with spherical shapes, with diameters close to 5, 10, 15, 20, 25, 50, 50, 60, 75, 90 and 100 nm, whose results are shown from (a) to (j) in Figure 10 corresponding to each diameter.
Based on the standard samples after training, the characterizations of the AuNPs were performed, determining the average wavelength, diameter, absorbance, and concentration of the NPs, which are presented in Table 1.
In Figure 11, the wavelength of the AuNPs in the standard samples was compared with the wavelengths obtained with AV implemented in LabVIEW.From the results, it is observed that the p-value is 0.338, which is higher than 0.05, so it is determined that the wavelength measured with the AV of the AuNPs is practically equal to the wavelength established in the standard samples.
Likewise, when comparing the diameters of the AuNPs measured concerning the standard samples using AV implemented in LabVIEW, a p-value of 0.879 was obtained, which indicates that the measured diameters fit with high precision to those established (Figure 12).
On the other hand, Figure 13 shows the relationship of the measured diameters with the absorbances and concentrations of AuNPs, where the measured peak plasmon resonance absorbance of the different AuNPs samples as a function of their diameter "d" can be represented by A spr = -0.0144d+ 2.2852, for an R² = 0.962.While the concentration of the AuNPs is represented by C Au = -0.0067d+ 0.7321, for an R² = 0.911.The implementation of AV in LabVIEW enabled accurate determination of the diameter of colloidal AuNPs.The ability to analyze images at different scales and the optimization of classbased algorithms for color classification were key to obtaining detailed and reliable measurements of the diameter of NPs that were in the range of approximately 5-100 nm.Also, a clear relationship was established between the wavelength of the incident light and the absorbance of the AuNPs.AV in LabVIEW facilitated the identification of spectral patterns, which contributed to understanding how the concentration of NPs affects the absorbance at different wavelengths.
With the implementation of Equation 2 (Haiss et al., 2007) on the LabVIEW platform, the concentration of colloidal AuNPs was calculated.This analysis is important to understand how changes in concentration can affect the optical response, which is valuable information for applications in sensors and optoelectronic devices.
The results obtained using AV implemented in LabVIEW were validated against conventional UV-Vis characterization methods.The consistency between the results demonstrates the reliability of the implemented system and its ability to provide accurate and comparable data at a low cost.

Figure 1 .
Figure 1.Diagram of colloidal AuNPs generation with Nd: YAG laser

Figure 9 .Figure 10 .
Figure 9. LabVIEW front panel of the artificial vision system for the characterization of AuNPs in colloidal state

Figure 11 .Figure 12 .Figure 13 .
Figure 11.Strip plots of the wavelength of AuNPs measured about the standard sample

Table 1 .
AuNPS pattern sample and average values of the AuNPs characterization