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Diatom–diazotroph associations (DDAs) are one of the most important symbiotic dinitrogen (N2) fixing groups in the oligotrophic ocean. Despite their capability to fix N2, ammonium (NH4+) remains a key nitrogen (N) source for DDAs, and the effect of NH4+ on their metabolism remains elusive. Here, we developed a coarse-grained, cellular model of the DDA with NH4+ uptake and quantified how the level of extracellular NH4+ influences metabolism and nutrient exchange within the symbiosis. The model shows that, under a fixed growth rate, an increased NH4+ concentration may lower the required level of N2 fixation and photosynthesis, and decrease carbon (C) and N exchange. A low-NH4+ environment leads to more C and N in nutrient exchange and more fixed N2 to support a higher growth rate. With higher growth rates, nutrient exchange and metabolism increased. Our study shows a strong effect of NH4+ on metabolic processes within DDAs, and thus highlights the importance of in situ measurement of NH4+ concentrations. 

The advent of chip calorimetry has enabled an unprecedented extension of the capability of differential scanning calorimetry to explore new domains of materials behavior. In this paper, we highlight some of our recent work: the application of heating and cooling rates above 104 K/s allows for the clear determination of the glass transition temperature, Tg, in systems where Tg and the onset temperature for crystallization, Tx, overlap; the evaluation of the delay time for crystal nucleation; the discovery of new polyamorphous materials; and the in-situ formation of glass in liquid crystals. From these application examples, it is evident that chip calorimetry has the potential to reveal new reaction and transformation behavior and to develop a new understanding. 

Abstract. Ocean color remote sensing is a challenging task over coastal watersdue to the complex optical properties of aerosols and hydrosols. Inorder to conduct accurate atmospheric correction, we previously implementeda joint retrieval algorithm, hereafter referred to as the Multi-Angular Polarimetric Ocean coLor (MAPOL) algorithm,to obtain the aerosol and water-leavingsignal simultaneously.The MAPOL algorithm has been validated with syntheticdata generated by a vector radiative transfer model, and good retrievalperformance has been demonstrated in terms of both aerosol and oceanwater optical properties (Gao et al., 2018).In this work we applied the algorithm to airborne polarimetricmeasurements from the Research Scanning Polarimeter (RSP) over bothopen and coastal ocean waters acquired in twofield campaigns: the Ship-Aircraft Bio-Optical Research (SABOR) in2014 and the North Atlantic Aerosols and Marine Ecosystems Study(NAAMES) in 2015 and 2016. Two different yet related bio-opticalmodels are designed for ocean water properties. One model aligns withtraditional open ocean water bio-optical models that parameterize theocean optical properties in terms of the concentration of chlorophyll a. The other is a generalized bio-optical model for coastal watersthat includes seven free parameters to describe the absorption andscattering by phytoplankton, colored dissolved organic matter, andnonalgal particles. The retrieval errors of both aerosol opticaldepth and the water-leaving radiance are evaluated. Through thecomparisons with ocean color data products from both in situmeasurements and the Moderate Resolution Imaging Spectroradiometer(MODIS), and the aerosol product from both the High SpectralResolution Lidar (HSRL) and the Aerosol Robotic Network (AERONET), the MAPOL algorithm demonstrates both flexibility and accuracy in retrievingaerosol and water-leaving radiance properties under various aerosoland ocean water conditions. 

Abstract. pH is an important property of aerosol particles but is difficult to measure directly. Several studies have estimated the pH values for fine particles in northern China winter haze using thermodynamic models (i.e., E-AIM and ISORROPIA) and ambient measurements. The reported pH values differ widely, ranging from close to 0 (highly acidic) to as high as 7 (neutral). In order to understand the reason for this discrepancy, we calculated pH values using these models with different assumptions with regard to model inputs and particle phase states. We find that the large discrepancy is due primarily to differences in the model assumptions adopted in previous studies. Calculations using only aerosol-phase composition as inputs (i.e., reverse mode) are sensitive to the measurement errors of ionic species, and inferred pH values exhibit a bimodal distribution, with peaks between −2 and 2 and between 7 and 10, depending on whether anions or cations are in excess. Calculations using total (gas plus aerosol phase) measurements as inputs (i.e., forward mode) are affected much less by these measurement errors. In future studies, the reverse mode should be avoided whereas the forward mode should be used. Forward-mode calculations in this and previous studies collectively indicate a moderately acidic condition (pH from about 4 to about 5) for fine particles in northern China winter haze, indicating further that ammonia plays an important role in determining this property. The assumed particle phase state, either stable (solid plus liquid) or metastable (only liquid), does not significantly impact pH predictions. The unrealistic pH values of about 7 in a few previous studies (using the standard ISORROPIA model and stable state assumption) resulted from coding errors in the model, which have been identified and fixed in this study.