# AI-Enabled Compact and Efficient Dynamic Light Scattering System for Precise Microparticle Sizing
## 1 Introduction  
Microparticles have become a cornerstone of modern nanotechnology, enabling revolutionary applications in fields such as biomedicine, energy storage, catalysis, and advanced materials [1]. Their unique physical and chemical properties are highly dependent on size, making accurate microparticle size characterization essential for both fundamental research and industrial applications [2]. A variety of microparticle characterization techniques are available, including transmission electron microscopy (TEM) [3], scanning electron microscopy (SEM) [4], atomic force microscopy (AFM) [5], and dynamic light scattering (DLS) [6]; however, these methods often face significant limitations. TEM and SEM provide high-resolution imaging and detailed morphological information but are time-consuming, require extensive sample preparation, and depend on costly, highly specialized equipment [7]. AFM, while capable of delivering precise three-dimensional surface profiles, suffers from low throughput and operational complexity, making it impractical for routine or large-scale measurements [8]. These limitations underscore the trade-offs between accuracy, cost, and accessibility in existing microparticle characterization methods. Among the various methods available, DLS has gained significant attention due to its non-invasive nature, ease of use, and ability to rapidly measure microparticle size distributions in colloidal suspensions [9]. By analyzing the intensity fluctuations of scatteredlight caused by Brownian motion [10], DLS provides a reliable estimate of particle size, particularly for monodisperse and polydisperse suspension systems [11; 12]. Its ability to handle samples in their native liquid environment further enhances its utility in microparticle research [13]. [PERSON] et al. investigated the effect of directional movement on DLS, demonstrating that the inclusion of the scattering wave vector variation due to particle directional motion significantly affects the intensity autocorrelation function (ACF) and improves the accuracy of particle size determination, especially in flowing dispersions [14]. [PERSON] et al. reviewed recent advancements in DLS imaging techniques for blood flow, highlighting their applications in brain vasculature assessment, skin perfusion monitoring, and non-invasive blood microcirculation characterization [15]. [PERSON] et al. introduced a novel probe head design that enables in-line monitoring of particle sizes in stirred colloidal suspensions using DLS, thus facilitating real-time process control [10]. [PERSON] et al. introduced an alternative method for processing DLS time series using spectral entropy and Artificial Neural Networks to predict particle size [9]. [PERSON] et al. reviewed the application of DLS as a powerful tool for in situ microparticle sizing, focusing on metal oxide microparticles and discussing its measuring principle, instrument design, and applications [12]. The application of artificial intelligence for particle size prediction in DLS systems, particularly for handling signals heavily affected by noise, remains an area that has yet to be fully explored. The previous studies primarily focused on the application of DLS but made minimal efforts to improve its methodology or address its inherent limitations [16]. Its reliance on the Stokes-Einstein equation to determine size necessitates the use of highly precise and sensitive instruments to reduce noise and ensure accuracy [15]. These instruments are often expensive (USD > 4000), sophisticated, and require meticulous maintenance, which can be a significant barrier to adoption for many laboratories and industries [17]. To address these challenges, there is a pressing need for novel microparticle size detection methods that combine high accuracy with affordability, ease of use, and scalability. Recent advancements in artificial intelligence (AI) have demonstrated immense potential in diverse scientific domains, including imaging, data analysis, and pattern recognition [18; 19; 20].