Catalytic Refining Lignin-Derived Monomers: Seesaw Effect between Nanoparticle and Single-Atom Pt.

Pt automatically adsorbed on oxygen vacancy of TiO2 via an in situ interfacial redox reaction, resulting in atomically dispersion of Pt on TiO2. In the upgrading of lignin-derived 4-propylguaiacol, single-atom catalyst (SAC) Pt/TiO2-H achieved a conversion of 96.9 % and a demethoxylation selectivity of 93.3 % under 3 MPa H2 at 250 °C for 3 h, markedly different from the performance of nanoparticle counterpart that gave deep deoxygenation selectivity over 99.0 %. The high demethoxylation activity of SAC Pt/TiO2-H is mainly attributed to its weak hydrogen spillover capacity that suppressed the benzene ring hydrogenation and the deep deoxygenation. Additionally, SAC Pt/TiO2-H reduced the energy barrier of CAr-OCH3 bond cleavage and accordingly lowered the Gibbs free energy of the demethoxylation reaction. This facile method could fabricate single-atom Au, Pd, Ir, and Ru supported on TiO2-H, demonstrating the generality of this strategy for the establishment of a library of SACs. Moreover, SAC exhibited versatile capacity in demethoxylation of different lignin-derived monomers and high stability. This study showcases the superiority of atomically dispersed metal catalysts for selective demethoxylation reactions and proposes a renewable alternative to fossil-based 4-alkylphenols through upgrading of lignin-derived monomers.

How many asymptomatic cases were unconfirmed in the US COVID-19 pandemic? The evidence from a serological survey.

A serological survey from CDC revealed more than 10% of individuals in America probably resolving or past infection with SARS-CoV-2 at the end of 2020, which illustrated there were massive unconfirmed asymptomatic infected people by contrast with the reported cases numbers. Asymptomatic patients as one of the crucial reasons for the COVID-19 pandemic being tough to contain, estimating the number of unconfirmed ones including the active infected and having cured in this population, is of great guiding significance for formulating epidemic prevention and control policies. This paper proposes a varying coefficient Susceptible-Infected-Removed-Susceptible (vSIRS) model to obtain the time series data of the unconfirmed asymptomatic infected numbers. Moreover, due to the time-varying coefficients, we can effectively track the situation changes of the COVID-19 intervened by related policy support and medical care level through this epidemiological model. A novel two-stage approach with a programming problem is correspondingly developed to accomplish the estimation of the unknown parameters in the vSIRS model. Subsequently, by leveraging seroprevalence data, daily reported cases data, and other clinical information, we apply the vSIRS model to analyze the evolution of COVID-19 in America. The modeling results show millions of active asymptomatic infected individuals were unconfirmed during the autumn and winter of 2020, which was a momentous factor for driving American COVID-19 pandemic.