Liquid-Liquid Phase Separation in Single Suspended Aerosol Microdroplets.

Liquid-liquid phase separation (LLPS) is ubiquitous in ambient aerosols. This specific morphology exerts substantial impacts on the physicochemical properties and atmospheric processes of aerosols, particularly on the gas-particle mass transfer, the interfacial heterogeneous reaction, and the surface albedo. Although there are many studies on the LLPS of aerosols, a clear picture of LLPS in individual aerosols is scarce due to the experimental difficulties of trapping a single particle and mimicking the suspended state of real aerosols. Here, we investigate the phase separation in individual contactless microdroplets by a self-constructed laser tweezer/Raman spectroscopy system. The dynamic transformation of the morphology of optically trapped droplets over the course of humidity cycles is detected by the time-resolved cavity-enhanced Raman spectra. The impacts of pH and inorganic components on LLPS in aerosols are discussed. The results show that the increasing acidity can enhance the miscibility between the hydrophilic and hydrophobic phases and decrease the separation relative humidity of aerosols. Moreover, the inorganic components also have various impacts on the aerosol phase state, whose influence depends on their different salting-out capabilities. It brings possible implications on the morphology of actual atmospheric particles, particularly for those dominated by internal mixtures of inorganic and organic components.

Sub-Cellular Dynamic Analysis of BGC823 Cells after Treatment with the Multi-Component Drug CKI Using Raman Spectroscopy.

Multi-component drugs (MCDs) can induce various cellular changes covering multiple levels, from molecular and subcellular structure to cell morphology. A "non-invasive" method for comprehensively detecting the dynamic changes of cellular fine structure and chemical components on the subcellular level is highly desirable for MCD studies. In this study, the subcellular dynamic processes of gastric cancer BGC823 cells after treatment with a multi-component drug, Compound Kushen Injection (CKI), were investigated using a homemade, high-resolution, confocal Raman spectroscopy (RS) device combined with bright-field imaging. The Raman spectra of the nucleus, cytoplasm and intracellular vesicles (0.4-1 µm) were collected simultaneously for each cell treated with CKI at different times and doses. The RS measurements showed that CKI decreased the DNA signatures, which the drug is known to inhibit. Meanwhile, the CKI-induced subcellular dynamic changes in the appearance of numerous intracellular vesicles and the deconstruction of cytoplasm components were observed and discussed. The results demonstrated that high-resolution subcellular micro-Raman spectroscopy has potential for detecting fine cellular dynamic variation induced by drugs and the screening of MCDs in cancer therapy.

The relative humidity-dependent viscosity of single quasi aerosol particles and possible implications for atmospheric aerosol chemistry.

Viscosity is a fundamental physicochemical property of aerosol particles that influences chemical evolution, mass transfer rates, particle formation, etc. and also changes with ambient relative humidity (RH). However, the viscosity of real individual aerosol particles still remains less understood. Here, we developed a novel optical system based on dual optical tweezers to measure the viscosity of single suspending aerosol droplets under different RH conditions. In our experiment, a pair of quasi atmospheric aerosol droplets composed of organic and inorganic chemical substances were trapped and levitated by dual laser beams, respectively, and then collided and coalesced. The backscattering light signals and bright-field images of the dynamic coalescence process were recorded to infer the morphological relaxation time and the diameter of the composited droplet. Then, the viscosity of the droplet was calculated based on these measured values. The ambient RH of the aerosol droplets was controlled by varying the relative flow rates of dry and humidified nitrogen gas in a self-developed aerosol chamber. The viscosities of single aqueous droplets nebulized with solutes of sucrose, various sulfates and nitrates, and organic/inorganic mixtures were measured over the atmospheric RH range. Besides, the viscosities of the proxies of actual ambient aerosols in Beijing were investigated, which reasonably interpreted the aerosol chemistry transforming from sulfate dominating to nitrate dominating at the PM10 (particulate matter with an aerodynamic diameter of less than 10 µm) level in the last decade in Beijing. Furthermore, the hygroscopicity of droplets with a solute of organic/inorganic mixtures was researched to obtain a deep insight into the relationship between the viscosity and mass transfer process. Hence, we provide a robust approach for investigating the viscosity and hygroscopicity of the actual individual liquid PM10 aerosols.

RamanNet: a lightweight convolutional neural network for bacterial identification based on Raman spectra.

Raman spectroscopy combined convolutional neural network (CNN) enables rapid and accurate identification of the species of bacteria. However, the existing CNN requires a complex hyperparameters model design. Herein, we propose a new simple network architecture with less hyperparameter design and low computation cost, RamanNet, for rapid and accurate identifying of bacteria at the species level based on its Raman spectra. We verified that compared with the previous CNN methods, the RamanNet reached comparable results on the Bacteria-ID Raman spectral dataset and PKU-bacterial Raman spectral datasets, but using only about 1/45 and 1/297 network parameters, respectively. RamanNet achieved an average isolate-level accuracy of 84.7 ± 0.3%, antibiotic treatment identification accuracy of 97.1 ± 0.3%, and distinguished accuracy of 81.6 ± 0.9% for methicillin-resistant and -susceptible Staphylococcus aureus (MRSA and MSSA) on the Bacteria-ID dataset, respectively. Moreover, it achieved an average accuracy of 96.04% on the PKU-bacterial dataset. The RamanNet model benefited from fewer model parameters that can be quickly trained even using CPU. Therefore, our method has the potential to rapidly and accurately identify bacterial species based on their Raman spectra and can be easily extended to other classification tasks based on Raman spectra.