Chemical potential and dimensions of chain molecules in athermal environments

SummaryRecombinant chimaeric molecules between tissue-type plasminogen activator (t-PA) and single chain urokinase-type plasminogen activator (scu-PA) or two chain urokinase-type plasminogen activator (tcu-PA) have intact enzymatic properties of scu-PA or tcu-PA towards natural and synthetic substrates (Nelles et al., J Biol Chem 1987; 262: 10855-10862). In the present study, we have compared the reactivity with inhibitors of both the single chain and two chain variants of recombinant u-PA and two recombinant chimaeric molecules between t-PA and scu-PA (t-PA/u-PA-s: amino acids 1-263 of t-PA and 144-411 of u-PA; t-PA/u-PA-e: amino acids 1-274 of t-PA and 138-411 of u-PA). Incubation with human plasma in the absence of a fibrin clot for 3 h at 37° C at equipotent concentrations (50% clot lysis in 2 h), resulted in significant fibrinogen breakdown (to about 40% of the normal value) for all two chain molecules, but not for their single chain counterparts. Preincubation of the plasminogen activators with plasma for 3 h at 37° C, resulted in complete inhibition of the fibrinolytic potency of the two chain molecules but did not alter the potency of the single chain molecules. Inhibition of the two chain molecules occurred with a t½ of approximately 45 min. The two chain variants were inhibited by the synthetic urokinase inhibitor Glu-Gly-Arg-CH2CCl with apparent second-order rate constants of 8,000-10,000 M−1s−1, by purified α2-antiplasmin with second-order rate constants of about 300 M−1s−1, and by plasminogen activator inhibitor-1 (PAI-1) with second-order rate constants of approximately 2 × 107 M−1s−1.It is concluded that the reactivity of single chain and two chain forms of t-PA/u-PA chimaers with inhibitors is very similar to that of the single and two chain forms of intact u-PA.

Download Full-text

Growth Kinetics of Quantum Size ZnO Particles

MRS Proceedings ◽

10.1557/proc-536-347 ◽

1998 ◽

Vol 536 ◽

Author(s):

E. M. Wong ◽

J. E. Bonevich ◽

P. C. Searson

Keyword(s):

Growth Kinetics ◽

Ostwald Ripening ◽

Chemical Potential ◽

Colloidal Suspensions ◽

Green Emission ◽

Blue Shift ◽

Constant Current ◽

Mass Model ◽

Zno Particles ◽

Kinetics Of

AbstractColloidal chemistry techniques were used to synthesize ZnO particles in the nanometer size regime. The particle aging kinetics were determined by monitoring the optical band edge absorption and using the effective mass model to approximate the particle size as a function of time. We show that the growth kinetics of the ZnO particles follow the Lifshitz, Slyozov, Wagner theory for Ostwald ripening. In this model, the higher curvature and hence chemical potential of smaller particles provides a driving force for dissolution. The larger particles continue to grow by diffusion limited transport of species dissolved in solution. Thin films were fabricated by constant current electrophoretic deposition (EPD) of the ZnO quantum particles from these colloidal suspensions. All the films exhibited a blue shift relative to the characteristic green emission associated with bulk ZnO. The optical characteristics of the particles in the colloidal suspensions were found to translate to the films.

Download Full-text

Scalable Approach to High Coverages on Oxides via Iterative Training of a Machine-Learning Algorithm

10.26434/chemrxiv.10288514.v1 ◽

2019 ◽

Author(s):

Andrew Medford ◽

Shengchun Yang ◽

Fuzhu Liu

Keyword(s):

Machine Learning ◽

Chemical Potential ◽

Learning Algorithm ◽

Absolute Error ◽

Low Energy ◽

Training Data ◽

High Coverage ◽

Metal Compounds ◽

Adsorption Energies ◽

The Stability

Understanding the interaction of multiple types of adsorbate molecules on solid surfaces is crucial to establishing the stability of catalysts under various chemical environments. Computational studies on the high coverage and mixed coverages of reaction intermediates are still challenging, especially for transition-metal compounds. In this work, we present a framework to predict differential adsorption energies and identify low-energy structures under high- and mixed-adsorbate coverages on oxide materials. The approach uses Gaussian process machine-learning models with quantified uncertainty in conjunction with an iterative training algorithm to actively identify the training set. The framework is demonstrated for the mixed adsorption of CHx, NHx and OHx species on the oxygen vacancy and pristine rutile TiO2(110) surface sites. The results indicate that the proposed algorithm is highly efficient at identifying the most valuable training data, and is able to predict differential adsorption energies with a mean absolute error of ~0.3 eV based on <25% of the total DFT data. The algorithm is also used to identify 76% of the low-energy structures based on <30% of the total DFT data, enabling construction of surface phase diagrams that account for high and mixed coverage as a function of the chemical potential of C, H, O, and N. Furthermore, the computational scaling indicates the algorithm scales nearly linearly (N1.12) as the number of adsorbates increases. This framework can be directly extended to metals, metal oxides, and other materials, providing a practical route toward the investigation of the behavior of catalysts under high-coverage conditions.

Download Full-text