ABSTRACT The ATOMS, standing for ALMA Three-millimeter Observations of Massive Star-forming regions, survey has observed 146 active star-forming regions with ALMA band 3, aiming to systematically investigate the spatial distribution of various dense gas tracers in a large sample of Galactic massive clumps, to study the roles of stellar feedback in star formation, and to characterize filamentary structures inside massive clumps. In this work, the observations, data analysis, and example science of the ATOMS survey are presented, using a case study for the G9.62+0.19 complex. Toward this source, some transitions, commonly assumed to trace dense gas, including CS J = 2−1, HCO+J = 1−0, and HCN J = 1−0, are found to show extended gas emission in low-density regions within the clump; less than 25 per cent of their emission is from dense cores. SO, CH3OH, H13CN, and HC3N show similar morphologies in their spatial distributions and reveal well the dense cores. Widespread narrow SiO emission is present (over ∼1 pc), which may be caused by slow shocks from large–scale colliding flows or H ii regions. Stellar feedback from an expanding H ii region has greatly reshaped the natal clump, significantly changed the spatial distribution of gas, and may also account for the sequential high-mass star formation in the G9.62+0.19 complex. The ATOMS survey data can be jointly analysed with other survey data, e.g. MALT90, Orion B, EMPIRE, ALMA_IMF, and ALMAGAL, to deepen our understandings of ‘dense gas’ star formation scaling relations and massive protocluster formation.

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In the present work, the effect of stationary phase resin chemistry and protein physicochemical properties on protein binding affinity in hydrophobic interaction chromatography (HIC) was investigated using linear gradient chromatography and quantitative structure-retention relationship (QSRR) modeling. Linear gradient experiments were carried out for a set of model proteins on four different HIC resins having different backbone and ligand chemistry. The retention data exhibited significant differences in protein binding affinity, not only across the phenyl and butyl ligand chemistries, but also for the different backbone chemistries found in the Sepharose (cross-linked agarose) and the Toyopearl 650 M (polymethacrylate) series of resins. QSRR models based on a Support Vector Machine (SVM) approach were developed for the linear retention data using molecular descriptors based on protein crystal structure and primary sequence information as well as a set of new hydrophobicity descriptors based on the solvent accessible protein surface area. The results indicate that the QSRR models were successfully able to capture and selectivity predict the changes observed in these systems. Furthermore, the new descriptors resulted in physically interpretable models of protein retention and provided insights into the factors influencing protein affinity in these different HIC systems. The approach put forth in this study provides a framework for developing predictive tools and for gaining insight into protein selectivity in hydrophobic interaction chromatography.