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Abstract Compared to H2-assisted activation mode, the direct dissociation of CO2 into carbonyl (*CO) with a simplified reaction route is advantageous for CO2-related synthetic processes and catalyst upgrading, while the stable C = O double bond makes it very challenging. Herein, we construct a subnano MoO3 layer on the surface of Mo2N, which provides a dynamically changing surface of MoO3↔MoO x (x < 3) for catalyzing CO2 hydrogenation. Rich oxygen vacancies on the subnano MoO x surface with a high electron donating capacity served as a scissor to directly shear the C = O double bond of CO2 to form CO at a high rate. The O atoms leached in CO2 dissociation are removed timely by H2 to regenerate active oxygen vacancies. Owing to the greatly enhanced dissociative activation of CO2, this MoO x /Mo2N catalyst without any supported active metals shows excellent performance for catalyzing CO2 hydrogenation to CO. The construction of highly disordered defective surface on heterostructures paves a new pathway for molecule activation.

Abstract The reverse water gas shift reaction can be considered as a promising route to mitigate global warming by converting CO2 into syngas in a large scale, while it is still challenging for non-Cu-based catalysts to break the trade-off between activity and selectivity. Here, the relatively high loading of Ni species is highly dispersed on hydroxylated TiO2 through the strong Ni and −OH interactions, thereby inducing the formation of rich and stable Ni clusters (~1 nm) on anatase TiO2 during the reverse water gas shift reaction. This Ni cluster/TiO2 catalyst shows a simultaneous high CO2 conversion and high CO selectivity. Comprehensive characterizations and theoretical calculations demonstrate Ni cluster/TiO2 interfacial sites with strong CO2 activation capacity and weak CO adsorption are responsible for its unique catalytic performances. This work disentangles the activity-selectivity trade-off of the reverse water gas shift reaction, and emphasizes the importance of metal−OH interactions on surface.

Constructing effective synergistic sites between multiple components in supported catalysts is the key to improve catalytic performance. Here the authors utilized the stress of MoO3/γ-Mo2N structure and the interaction between Pt and support to construct an effective catalytic interface for the low-temperature reverse water–gas shift reaction.

Constructing stable active sites in catalysts for high temperature catalytic reactions remains challenging. Here, the authors manage to make stable copper clusters in the copper‒ceria catalyst with high Cu loading (15 wt.%) for the high-temperature reverse water gas shift reaction.

Cell adhesion molecules (CAM) are crucial in several pathological inflammation processes in Alzheimer’s disease (AD). However, their potential for clinical diagnostics remains unknown. The present investigation evaluated the clinical significance of ALCAM, VCAM-1, NCAM, and ICAM-1 levels in the plasma of participants with cognitive impairment (44 patients with mild cognitive impairment, 71 patients with Alzheimer’s dementia, and 18 patients with other dementia) and 28 controls with normal cognitive ability. We also detected plasma levels of multiple inflammatory factors (IFN-gamma, IL-18, IL-1beta, IL-13, IL-8, IL-7, CCL11, MCP-1, TSLP, IL-10, BDNF, IL-17, IL-5, TREM-1) using Multiplex liquid chip and plasma levels of Abeta1-42 and Abeta1-40 using liquid-phase flow cytometry (FCM). Our findings demonstrated a correlation of ALCAM and VCAM-1 with age, the severity of cognitive decline, and MTA, but no significant difference between groups for NCAM and ICAM-1. ALCAM and VCAM-1 both demonstrated a positive correlation with the degree of atrophy in the medial temporal lobe structure. Further analysis revealed no significant correlation in plasma between VCAM-1, ALCAM and Abeta1-40, Abeta1-42. Nevertheless, there was a significant correlation between VCAM-1, ALCAM and many inflammatory factors. Furthermore, the predictive value of ALCAM and VCAM-1 for AD was assessed using a multi-parameter regression model. ALCAM and VCAM-1 in combination with ApoE4, education, age, and MMSE could predict AD with high precision (AUC=0.891; AIC=146.9) without imaging diagnosis. ALCAM and VCAM-1 combination improved the predictive accuracy significantly. In a nutshell, these findings revealed ALCAM and VCAM-1 as reliable indicators of Alzheimer’s disease.