Bain Alison, Prisle Nønne L, Bzdek Bryan R
School of Chemistry, University of Bristol, Cantock's Close, Bristol BS8 1TS, U.K.
Department of Chemistry, Oregon State University, 2100 SW Monroe Ave, Corvallis, Oregon 97331, United States.
ACS Earth Space Chem. 2024 Oct 22;8(11):2244-2255. doi: 10.1021/acsearthspacechem.4c00199. eCollection 2024 Nov 21.
Surfactants are important components of atmospheric aerosols, potentially impacting their hygroscopic growth and eventual activation into cloud droplets. By adsorbing at the air-water interface, surfactants lower the surface tension of aqueous systems. However, in microscopic aerosol droplets, the bulk surfactant concentration can become depleted because of the droplets' high surface-area-to-volume ratio, reducing the bulk surfactant concentration at equilibrium and increasing droplet surface tension. Partitioning models have been developed to account for the concentration- and size-dependencies of surface tension, but these models have rarely been assessed against experimentally measured droplet surface tensions. Here, we directly compare surface tension predictions made using a simple kinetic partitioning model and a thermodynamic monolayer partitioning model against experimentally measured picoliter droplet surface tensions for 12 surfactant-cosolute systems. Surface tension predictions were also made across 8 orders of magnitude in droplet radius. The largest differences between model predictions were associated with the predicted onset of bulk depletion. The quality of the isotherm or parametrization fit to the macroscopic data most strongly influenced a model's ability to accurately predict droplet surface tension. These results highlight the importance of validating partitioning models against droplet surface tension measurements in size ranges where bulk depletion is expected to occur and motivate collection of high-quality macroscopic surface tension data sets that serve as model inputs. The results also validate both models' abilities to predict aerosol surface tension across size and composition, which will facilitate their eventual incorporation into cloud parcel models to explore the impact of surface tension assumptions on cloud droplet number concentration.
表面活性剂是大气气溶胶的重要组成部分，可能会影响其吸湿增长以及最终激活成为云滴。通过吸附在气-水界面，表面活性剂降低了水体系的表面张力。然而，在微观气溶胶液滴中，由于液滴的高表面积与体积比，本体表面活性剂浓度可能会耗尽，从而降低平衡时的本体表面活性剂浓度并增加液滴表面张力。已经开发了分配模型来解释表面张力对浓度和尺寸的依赖性，但这些模型很少根据实验测量的液滴表面张力进行评估。在这里，我们直接将使用简单动力学分配模型和热力学单分子层分配模型做出的表面张力预测与针对12种表面活性剂-共溶质体系实验测量的皮升液滴表面张力进行比较。还对液滴半径跨越8个数量级的情况进行了表面张力预测。模型预测之间的最大差异与预测的本体耗尽起始有关。等温线或参数化与宏观数据的拟合质量对模型准确预测液滴表面张力的能力影响最大。这些结果突出了在预期会发生本体耗尽的尺寸范围内，根据液滴表面张力测量结果验证分配模型的重要性，并促使收集高质量的宏观表面张力数据集作为模型输入。结果还验证了这两个模型在预测不同尺寸和组成的气溶胶表面张力方面的能力，这将有助于它们最终纳入云块模型，以探索表面张力假设对云滴数浓度的影响。
