Potential constraints on protocell size are developed from simple entropic considerations. To do that, two new different indexes as measures of their structural and dynamic order were developed and applied to an elemental model of the heterotrophic protocell. According to our results, cell size should be a key factor determining the potential of these primitive systems to evolve and consequently to support life. Our analyses also suggest that the size of the optimal vesicles could be constrained to have radii in the interval [Formula: see text], where the two extreme limits [Formula: see text] and [Formula: see text] represent the states of maximum structural order (largest accumulation of substrate inside the vesicle) and the maximum flux of entropy production, respectively. According to the above criteria, the size of the optimum vesicles falls, approximately, in the same spatial range estimated for biological living cells assuming plausible values for the second-order rate constant involved in the catabolic process. Furthermore, the existence of very small vesicles could be seriously affected by the limited efficiency, far from the theoretical limits, with which these catabolic processes may proceed in a prebiotic system.
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http://dx.doi.org/10.1007/s00249-019-01359-2 | DOI Listing |
Natl Sci Rev
January 2025
Institute for Advanced Study, Tsinghua University, Beijing 100084, China.
In closed systems, the celebrated Lieb-Schultz-Mattis (LSM) theorem states that a one-dimensional locally interacting half-integer spin chain with translation and spin rotation symmetries cannot have a non-degenerate gapped ground state. However, the applicability of this theorem is diminished when the system interacts with a bath and loses its energy conservation. In this letter, we propose that the LSM theorem can be revived in the entanglement Hamiltonian when the coupling to the bath renders the system short-range correlated.
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January 2025
Beijing National Laboratory for Molecular Sciences, CAS Laboratory of Colloid and Interface and Thermodynamics, CAS Research/Education Centre for Excellence in Molecular Sciences, Centre for Carbon Neutral Chemistry, Institute of Chemistry, Chinese Academy of Sciences, Beijing 100190, China; School of Chemistry, University of Chinese Academy of Sciences, Beijing 100049, China; Shanghai Key Laboratory of Green Chemistry and Chemical Processes, School of Chemistry and Molecular Engineering, East China Normal University, Shanghai 200062, China. Electronic address:
Temperature affects both the thermodynamics of intermediate adsorption and the kinetics of elementary reactions. Despite its extensive study in thermocatalysis, temperature effect is typically overlooked in electrocatalysis. This study investigates how electrolyte temperature influences CO electroreduction over Cu catalysts.
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January 2025
Saint Petersburg State University, St. Petersburg, 198504, Russia.
Using angle-resolved photoemission spectroscopy (ARPES) and density functional theory (DFT), an experimental and theoretical study of changes in the electronic structure (dispersion dependencies) and corresponding modification of the energy band gap at the Dirac point (DP) for topological insulator (TI) [Formula: see text] have been carried out with gradual replacement of magnetic Mn atoms by non-magnetic Ge atoms when concentration of the latter was varied from 10% to 75%. It was shown that when Ge concentration increases, the bulk band gap decreases and reaches zero plateau in the concentration range of 45-60% while trivial surface states (TrSS) are present and exhibit an energy splitting of 100 and 70 meV in different types of measurements. It was also shown that TSS disappear from the measured band dispersions at a Ge concentration of about 40%.
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January 2025
Department of Mathematical Sciences, Faculty of Science, Somali National University, Mogadishu Campus, Mogadishu, Somalia.
In recent years, machine learning has gained substantial attention for its ability to predict complex chemical and biological properties, including those of pharmaceutical compounds. This study proposes a machine learning-based quantitative structure-property relationship (QSPR) model for predicting the physicochemical properties of anti-arrhythmia drugs using topological descriptors. Anti-arrhythmic drug development is challenging due to the complex relationship between chemical structure and drug efficacy.
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January 2025
Torrens University Australia, Fortitude Valley, QLD 4006, Leaders Institute, 76 Park Road, Woolloongabba, QLD 4102, Brisbane, Queensland, Australia.
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