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In experiments, the distributions of mRNA or protein numbers in single cells are often fitted to the random telegraph model which includes synthesis and decay of mRNA or protein, and switching of the gene between active and inactive states. While commonly used, this model does not describe how fluctuations are influenced by crucial biological mechanisms such as feedback regulation, non-exponential gene inactivation durations, and multiple gene activation pathways. Here we investigate the dynamical properties of four relatively complex gene expression models by fitting their steady-state mRNA or protein number distributions to the simple telegraph model. We show that despite the underlying complex biological mechanisms, the telegraph model with three effective parameters can accurately capture the steady-state gene product distributions, as well as the conditional distributions in the active gene state, of the complex models. Some effective parameters are reliable and can reflect realistic dynamic behaviors of the complex models, while others may deviate significantly from their real values in the complex models. The effective parameters can also be applied to characterize the capability for a complex model to exhibit multimodality. Using additional information such as single-cell data at multiple time points, we provide an effective method of distinguishing the complex models from the telegraph model. Furthermore, using measurements under varying experimental conditions, we show that fitting the mRNA or protein number distributions to the telegraph model may even reveal the underlying gene regulation mechanisms of the complex models. The effectiveness of these methods is confirmed by analysis of single-cell data forE. coliand mammalian cells. All these results are robust with respect to cooperative transcriptional regulation and extrinsic noise. In particular, we find that faster relaxation speed to the steady state results in more precise parameter inference under large extrinsic noise.

 

Ethylene glycol is a widely utilized commodity chemical, the production of which accounts for over 46 million tons of CO₂emission annually. Here we report a paired electrocatalytic approach for ethylene glycol production from methanol. Carbon catalysts are effective in reducing formaldehyde into ethylene glycol with a 92% Faradaic efficiency, whereas Pt catalysts at the anode enable formaldehyde production through methanol partial oxidation with a 75% Faradaic efficiency. With a membrane-electrode assembly configuration, we show the feasibility of ethylene glycol electrosynthesis from methanol in a single electrolyzer. The electrolyzer operates a full cell voltage of 3.2 V at a current density of 100 mA cm⁻², with a 60% reduction in energy consumption. Further investigations, using operando flow electrolyzer mass spectroscopy, isotopic labeling, and density functional theory (DFT) calculations, indicate that the desorption of a *CH₂OH intermediate is the crucial step in determining the selectively towards ethylene glycol over methanol.

 

Electrocatalytic conversion of carbon dioxide to valuable chemicals and fuels driven by renewable energy plays a crucial role in achieving net-zero carbon emissions. Understanding the structure–activity relationship and the reaction mechanism is significant for tuning electrocatalyst selectivity. Therefore, characterizing catalyst dynamic evolution and reaction intermediates under reaction conditions is necessary but still challenging. We first summarize the most recent progress in mechanistic understanding of heterogeneous CO₂/CO reduction using in situ/operando techniques, including surface-enhanced vibrational spectroscopies, X-ray- and electron-based techniques, and mass spectroscopy, along with discussing remaining limitations. We then offer insights and perspectives to accelerate the future development of in situ/operando techniques.

 

Nanostructured Cu catalysts have increased the selectivities and geometric activities for high value C–C coupled (C 2 ) products (ethylene, ethanol, and acetate) in the electrochemical CO (2) reduction reaction (CO (2) RR). The selectivity among the high-value C 2 products is also altered, where for instance the yield of acetate increases with alkalinity and is dependent on the catalyst morphology. The reaction mechanisms behind the selectivity towards acetate vs. other C 2 products remain controversial. In this work, we elucidate the reaction mechanism for acetate formation by using ab initio simulations, a coupled kinetic-transport model, and loading dependent experiments. We find that trends in acetate selectivity can be rationalized from variations in electrolyte pH and the local mass transport properties of the catalyst and not from changes in Cu's intrinsic activity. The selectivity mechanism originates from the transport of ketene, a stable (closed shell) intermediate, away from the catalyst surface into solution where it reacts to form acetate. While this type of mechanism has not yet been discussed in the CO (2) RR, variants of it may explain similar selectivity fluctuations observed for other stable intermediates like CO and acetaldehyde. Our proposed mechanism suggests that acetate selectivity increases with increasing pH, decreasing catalyst roughness and significantly varies with the applied potential. 

Abstract Artificial photosynthesis systems are proposed as an efficient alternative route to capture CO 2 to produce additional food for growing global demand. Here a two-step CO 2 electrolyser system was developed to produce a highly concentrated acetate stream with a 57% carbon selectivity (CO 2 to acetate), allowing its direct use for the heterotrophic cultivation of yeast, mushroom-producing fungus and a photosynthetic green alga, in the dark without inputs from biological photosynthesis. An evaluation of nine crop plants found that carbon from exogenously supplied acetate incorporates into biomass through major metabolic pathways. Coupling this approach to existing photovoltaic systems could increase solar-to-food energy conversion efficiency by about fourfold over biological photosynthesis, reducing the solar footprint required. This technology allows for a reimagination of how food can be produced in controlled environments.