Low-Temperature Water Uptake of Individual Marine and Biologically Relevant Atmospheric Particles Using Micro-Raman Spectroscopy

Vertical connectivity of microbiome and metabolome reveals depth-dependent variations across a deep cold-seep water column

Deciphering the vertical connectivity of oceanic microbiome and metabolome is crucial for understanding the carbon sequestration and achieving the carbon neutrality. However, we lack a systematic view of the interplay among particle transport, microbial community, and metabolic trait across depths. Through integrating the biogeochemical, microbial, and metabolic characteristics of a deep cold-seep water column (∼1989 m), we find the altered connectivity of microbial community and dissolved organic matter (DOM) across depths. Both the microbial communities (bacteria and protists) and DOM show a clear compositional connectivity from surface to the depth of 1000 m, highlighting the controls of sinking particle over microbial connectivity from the epipelagic to mesopelagic zone. However, due to the biological migration and ocean mixing, the fecal-associated bacteria and protistan consumers unexpectedly emerge and the degradation index of DOM substantially alters around 1000-1200 m. Collectively, we unveil the significance of multi-faceted particle dispersion, which supports the connectivity and variability of deep ocean microbial communities.

Revealing the Impacts of Chemical Complexity on Submicrometer Sea Spray Aerosol Morphology

Sea spray aerosol (SSA) ejected through bursting bubbles at the ocean surface is a complex mixture of salts and organic species. Submicrometer SSA particles have long atmospheric lifetimes and play a critical role in the climate system. Composition impacts their ability to form marine clouds, yet their cloud-forming potential is difficult to study due to their small size. Here, we use large-scale molecular dynamics (MD) simulations as a "computational microscope" to provide never-before-seen views of 40 nm model aerosol particles and their molecular morphologies. We investigate how increasing chemical complexity impacts the distribution of organic material throughout individual particles for a range of organic constituents with varying chemical properties. Our simulations show that common organic marine surfactants readily partition between both the surface and interior of the aerosol, indicating that nascent SSA may be more heterogeneous than traditional morphological models suggest. We support our computational observations of SSA surface heterogeneity with Brewster angle microscopy on model interfaces. These observations indicate that increased chemical complexity in submicrometer SSA leads to a reduced surface coverage by marine organics, which may facilitate water uptake in the atmosphere. Our work thus establishes large-scale MD simulations as a novel technique for interrogating aerosols at the single-particle level.

Research on the Comprehensive Evaluation Method for the Automatic Recognition of Raman Spectrum under Multidimensional Constraint

Raman spectrum contains abundant substance information with fingerprint characteristics. However, due to the huge variety of substances and their complex characteristic information, it is difficult to recognize the Raman spectrum accurately. Starting from dimensions like the Raman shift, the relative peak intensity, and the overall hit ratio of characteristic peaks, we extracted and recognized the characteristics in the Raman spectrum and analyzed these characteristics from local and global perspectives and then proposed a comprehensive evaluation method for the recognition of Raman spectrum on the basis of the data fusion of the recognition results under multidimensional constraint. Based on the common spectrum database of the normal Raman and surface-enhanced Raman of thousands of substances, we analyzed the performance of the evaluation method. It shows that even for the identification of spectra from instruments of low technical specifications, the automatic recognition rate of the sample can reach 98% and above, a great improvement compared with that of the common identification algorithms, which proves the effectiveness of the comprehensive evaluation method under multidimensional constraint.