Critical cluster composition from homogeneous nucleation data: application to water in carbon dioxide–nitrogen carrier gases

Knowledge on critical cluster composition is important for improving the nucleation theory. Thus, homogeneous water nucleation experiments previously carried out in nitrogen and 0%, 5%, 15% and 25% of carbon dioxide ( Campagna et al. 2020a, 2021) are analyzed. The tests were conducted at 240 K and 0.1 MPa, 1 MPa and 2 MPa. The observed nucleation rates are strongly dependent on supersaturation, pressure, temperature and mixture composition. These experimentally found dependencies can be used to derive the composition of critical clusters by means of the nucleation theorem. In this way, a macroscopic quantity, nucleation rate, reveals properties of critical clusters consisting of a few tens of molecules. Two novel methods are presented for the detailed application of the nucleation theorem. The first method extends to mixtures of $$\,\,\,\,\,\,\,N>2\,\,\,\,\,\,$$ N > 2 components the approach used in literature for two components. The second method not only applies to $$N>2$$ N > 2 mixtures in a more straightforward manner, but it can also be used for unary as well as for binary and multi-component nucleation cases. To the best of our knowledge, for the first time the critical cluster composition is computed for high pressure nucleation data of a vapor (here water) in mixtures of two carrier gases (here carbon dioxide–nitrogen). After a proper parameterization of the nucleation rate data, both methods consistently lead to the same critical nuclei compositions within the experimental uncertainty. Increasing pressure and carbon dioxide molar fraction at fixed supersaturation leads to a decrease in the water content of the critical cluster, while the adsorbed number of nitrogen and carbon dioxide molecules increases. As a consequence, the surface tension decreases. This outcome explains the observed increase in the nucleation rate with increasing pressure and carbon dioxide molar fraction at constant supersaturation. Graphic abstract

Cluster Models of FeMoco with Sulfide and Carbyne Ligands: Effect of Interstitial Atom in Nitrogenase Active Site

<p>Nitrogen-fixing organisms perform dinitrogen reduction to ammonia at an iron-M (M = Mo, Fe, or V) cofactor (FeMco) of nitrogenase. FeMoco displays eight metal centers bridged by sulfides and a carbide having the MoFe₇S₈C cluster composition. The role of the carbide ligand, a unique motif in protein active sites, remains poorly understood. Toward addressing its function, we isolated synthetic models of subsite MFe₃S₃C displaying sulfides and a carbyne ligand. We developed synthetic protocols for structurally related clusters, [Tp*MFe₃S₃X]^n-, where M = Mo or W, the bridging ligand X = CR, N, NR, S, and Tp* = tris(3,5-dimethyl-1-pyrazolyl)hydroborate, to study the effects of the identity of the heterometal and the bridging X group on structure and electrochemistry. While the nature of M results in minor changes, the μ₃-bridging ligand X has a large impact on reduction potentials, with differences higher than 1 V, even for the same formal charge, the most reducing clusters being supported by the carbyne ligand. </p>

Cluster Models of FeMoco with Sulfide and Carbyne Ligands: Effect of Interstitial Atom in Nitrogenase Active Site

<p>Nitrogen-fixing organisms perform dinitrogen reduction to ammonia at an iron-M (M = Mo, Fe, or V) cofactor (FeMco) of nitrogenase. FeMoco displays eight metal centers bridged by sulfides and a carbide having the MoFe₇S₈C cluster composition. The role of the carbide ligand, a unique motif in protein active sites, remains poorly understood. Toward addressing its function, we isolated synthetic models of subsite MFe₃S₃C displaying sulfides and a carbyne ligand. We developed synthetic protocols for structurally related clusters, [Tp*MFe₃S₃X]^n-, where M = Mo or W, the bridging ligand X = CR, N, NR, S, and Tp* = tris(3,5-dimethyl-1-pyrazolyl)hydroborate, to study the effects of the identity of the heterometal and the bridging X group on structure and electrochemistry. While the nature of M results in minor changes, the μ₃-bridging ligand X has a large impact on reduction potentials, with differences higher than 1 V, even for the same formal charge, the most reducing clusters being supported by the carbyne ligand. </p>

Nucleation pathways in barium silicate glasses

Nucleation is generally viewed as a structural fluctuation that passes a critical size to eventually become a stable emerging new phase. However, this concept leaves out many details, such as changes in cluster composition and competing pathways to the new phase. In this work, both experimental and computer modeling studies are used to understand the cluster composition and pathways. Monte Carlo and molecular dynamics approaches are used to analyze the thermodynamic and kinetic contributions to the nucleation landscape in barium silicate glasses. Experimental techniques examine the resulting polycrystals that form. Both the modeling and experimental data indicate that a silica rich core plays a dominant role in the nucleation process.

Improved identification of evaporation rates and thermodynamic data by Monte-Carlo method

&lt;p&gt;Atmospheric new particle formation and successive cluster growth to aerosol particles is an important field of research, in particular due to climate change phenomena and air quality monitoring. Recent developments in the instrumentation have enabled quantification of ionic clusters formed in the gas phase at the first steps of particle formation under atmospherically relevant mixing ratios. However, electrically neutral clusters are prevalent in atmospheric conditions, and thus must be charged prior to detection by mass spectrometer. The charging process can lead to cluster fragmentation and thus alter the measured cluster composition.&lt;/p&gt;&lt;p&gt;Even when the cluster composition can be measured directly, this does not quantify individual cluster-level properties, such as cluster collision and evaporation rates. Collision rates contain relatively small uncertainties in comparison to evaporation rates, which are computed using detailed balance assumption together with the free energies of cluster formation, which can in turn be obtained from Quantum chemistry (QC) methods. As evaporation rates depend exponentially on the free energies, even difference by several kcal/mol between different QC methods results in orders of magnitude differences in evaporation rates.&lt;/p&gt;&lt;p&gt;On the other hand, in spite of the error margins associated with the evaporation rates, simulations of cluster populations, which incorporate collision and evaporation rates as free parameters (such as Becker-D&amp;#246;ring models), have demonstrated good qualitative agreement with experimental data. The Becker-D&amp;#246;ring equations are a system of Ordinary Differential equations (ODE) which account for cluster birth and death processes, as well as external sinks and sources. In mathematical terms, prediction of cluster concentrations using kinetic simulations with given cluster collision and evaporation rates is called a forward problem.&lt;/p&gt;&lt;p&gt;In the present study, we focus on the so-called inverse problem of how to derive the evaporation rates and thermodynamic data (enthalpy change and entropy change due to addition or removal of molecule) from available measurements, rather than on the forward problem. We do this by Delayed Rejection Adaptive Monte Carlo (DRAM) method for the system containing sulfuric acid and ammonia with the maximal size of the pentamer. Initially, we tested the method on the synthetic data created from Atmospheric Cluster Dynamic Code (ACDC) simulations. By so doing, we identify the combination of fitted parameters and concentration measurements, which leads to the best identification of the evaporation rates. Additionally, we demonstrated that the temperature-dependent data yield better estimates of the evaporation rates as compared to the time-dependent data measured before the system has reached the steady state.&lt;/p&gt;&lt;p&gt;Next, we apply the technique to improve the identification of the evaporation rates from CLOUD chamber data, which contain cluster concentrations and new particle formation rates measured at different temperatures and a wide range of atmospherically relevant sulfuric acid and ammonia concentrations. As a result, we were able to obtain the probability density functions (PDFs) that show small standard variations for thermodynamic data. By using the values from the PDFs as parameters in the ACDC model, we achieve a fair agreement with the measured NPFs and cluster concentrations for a wide range of temperatures.&lt;/p&gt;

Sensitive and non-invasive cholesterol determination in saliva via optimization of enzyme loading and platinum nano-cluster composition

To provide rapid and accurate determination of cholesterol, we have developed a simple, disposable, enzyme-based salivary cholesterol biosensor.