# AI-Enabled Compact and Efficient Dynamic Light Scattering System for Precise Microparticle Sizing
## 3 Results and Discussion  
Our work addresses several traditional limitations of DLS technology through the introduction of key innovations. First, we significantly reduce the cost associated with conventional DLS hardware by utilizing low-cost equipment, providing a cost-effective solution for microparticle size characterization. Second, to address the challenge of noise interference in machine learning applications, we applied PCA to high-noise ACF signals and developed a novel approach that integrates GADF, GASF, and CNN. This method demonstrates robust performance in accurately predicting particle sizes, even under noisy conditions. Third, we mitigate the time-intensive nature of traditional DLS measurements by incorporating a pre-trained model, allowing for rapid measurements within just 13 s, which marks a significant advancement in efficiency. Furthermore, our device is designed with enhanced portability and space efficiency, weighing only 5 kg with a compact volume of 0.08 m\({}^{3}\), in stark contrast to commercial DLS systems that typically weigh 30 kg and occupy a volume of 0.18 m\({}^{3}\). Figure 1a illustrates the conceptual design of the AI-DLS system, which consists of a helium-neon laser (632.8 nm, 2 mW), two apertures, a focusing lens, a silicon photodiode detector, a quartz cuvette, and a data acquisition card (DAQ). Figure 1b presents the corresponding 3D model, designed with a 90\({}^{\circ}\) scattering measurement angle and fabricated using acrylic sheets and 3D-printed molds. The system components, including the laser, lens, and cuvette, were meticulously assembled to create the physical prototype shown in Figure 1c. The prototype features a compact footprint of 0.05 m\({}^{2}\), a height of 0.16 m, and weighs 5 kg. This represents only 4.5% of the volume (0.18 m\({}^{3}\)) and 16.7% of the weight (30 kg) of conventional DLS instruments [26]. Additionally, the AI-DLS system costs USD \(\sim\)1000, just 6.3% cost of traditional models (USD 16,000) [26]. This substantial reduction in size, weight, and cost highlights the significant advancements achieved with the AI-DLS system. The laser beam is directed through a sample comprising particles suspended in a liquid medium. As the particles undergo thermal motion, they diffuse and scatter light in all directions [6]. The temporal fluctuations in the scattered light intensity are captured by a photodetector [14]. These fluctuations are intrinsically linked to the particles' diffusion coefficients, which are inversely proportional to their sizes [1; 16]. The ACF, mathematically denoted as g(\(\tau\)), is derived from the time series data of scattered light intensity. Here, \(\tau\) signifies the lag time between successive intensity measurements [27]. The ACF quantifies the correlation between scattered light intensities at different time points separated by \(\tau\). For a monodisperse sample, the ACF exhibits an exponential decay with increasing \(\tau\), with the decay rate directly correlating to the particles' diffusion coefficient. By fitting the experimental ACF to a theoretical model, the diffusion coefficient can be extracted, and subsequently, the particle size can be determined using the Stokes-Einstein equation [28] for traditional DLS. Figure 1: (a) The configuration, (b) 3D model, and (c) photos of the AI-DLS. Monodisperse polystyrene (PS) spheres with diameters of 100-1500 nm were employed as test particles to assess the accuracy of the AI-DLS system. The diameters were confirmed via SEM. Measurements were conducted for 15 microsphere sizes, ranging from 0 nm (pure water as the reference) to 1500 nm, in 100 nm increments. Each size was measured five times with 10 s intervals, generating 80 datasets for ACF calculation and subsequent analysis.