The regulation mechanism of a zero-dimensional interface towards a catalytic reaction in the setting of a single-atom catalyst has been elusive to researchers. In a recent article published in Journal of the American Chemical Society, Zeng and Zhou et al. differentiated the electronic and steric effects on the oxygen evolution reaction at two distinct zero-dimensional interfaces. The steric interaction resulted in the desired adsorption behavior of intermediates at the interface, which lowered the energy barrier to the rate-determining step (RDS) and thus facilitated the oxygen evolution reaction. For the first time, this work validated the impacts of electronic and steric effects on the atomic interface of catalysts by delicately designing the anchoring site of single atoms on the support. The elegant design concept presented in this work pushes the research field of interface engineering to the atomic level and blazes a trail for the rational development of high-performing catalysts.

Investigations of strongly correlated quantum impurity systems (QIS), which exhibit diversified novel and intriguing quantum phenomena, have become a highly concerning subject in recent years. The hierarchical equations of motion (HEOM) method is one of the most popular numerical methods to characterize QIS linearly coupled to the environment. This review provides a comprehensive account of a formally rigorous and numerical convergent HEOM method, including a modeling description of the QIS and an overview of the fermionic HEOM formalism. Moreover, a variety of spectrum decomposition schemes and hierarchal terminators have been proposed and developed, which significantly improve the accuracy and efficiency of the HEOM method, especially in cryogenic temperature regimes. The practicality and usefulness of the HEOM method to tackle strongly correlated issues are exemplified by numerical simulations for the characterization of nonequilibrium quantum transport and strongly correlated Kondo states as well as the investigation of nonequilibrium quantum thermodynamics.

Inorganic chiral nanomaterials have attracted wide attention because of their superior physical properties and chiroptical activities. Great progress in chiral nanostructure preparation has been made, such as noble metals and semiconductors. In this review, we introduce several chiral nanomaterials with feasible biocompatibility and low cytotoxicity that are promising candidates for biological applications, and we focus on their preparation in terms of their circular dichroism (CD) effects and circular luminescence properties. Additionally, we summarize the working function of chiral nanostructures toward some common diseases with high prevalence, such as Alzheimer’s disease (AD), Parkinson’s disease (PD), diabetes and even cancers. The introduction of inorganic chirality will provide a novel way to diagnose and treat these diseases.

Developing nobel-metal-free catalysts, especially for iron-nitrogen on carbon (FeNC) materials, has been an urgent demand for wide applications of proton exchange membrane fuel cells (PEMFCs). However, the inferior oxygen reduction reaction (ORR) activity of traditional iron-nitrogen sites in acidic conditions seriously impedes the further improvement of their performance. Herein, we synthesized FeN₄ with NO (nitric oxide) group axial modification (denoted as NO-FeN₄) on a large scale through a confined small molecule synthesis strategy. Benefitting from the strong electron-withdrawing effect of the NO group, the central electron-rich FeN₄ site exhibits ultrahigh ORR activity with a three times higher mass activity (1.1 A·g⁻¹ at 0.85 V) compared to the traditional FeN₄ sample, as well as full four-electron reaction selectivity. Moreover, the PEMFC assembled with the as-prepared electrocatalyst also exhibits a greatly enhanced peak power density (>725 mW·cm⁻²). This work provides a new approach to rationally design advanced M-N_x nonnoble electrocatalysts for the ORR.

A new smart supramolecular polypeptide copolymer P(Glu-co-Lys) was synthesized by the polymerization of α-amino acids using the N-thiocarboxylic acid anhydride (NTA) method, using the pH dynamic response peptide of L-glutamic acid and L-lysine as a carrier for tumor cells. The drug delivery system activated by external acid can self-assemble (pH 7.4) and disassemble (pH 5.5) under the adjustment of pH to load the drug and control its release. Doxycycline (DOX) and the photothermal reagent hydrophilic quanternary stereo-cyanine (HQS-Cy) were loaded into the peptide copolymer to obtain HQS-Cy/DOX nanoparticles (NPs) for chemo-photothermal therapy. Gentle photothermal heating can enhance the absorption of drugs by cells and enhance the efficacy of chemotherapy. In addition, chemo-photothermal therapy can solve the defect of easy recurrence after single photothermal therapy. The ingenious nanodrug delivery system of HQS-Cy/DOX NPs provides great potential for the improvement of chemo-photothermal therapy and will achieve excellent therapeutic effects in cancer treatment.

CLC-7 functions as a Cl⁻/H⁺ exchanger in lysosomes. Defects in CLC-7 and its β-subunit, Ostm1, result in osteopetrosis and neurodegeneration. Here, we present the cryogenic electron microscopy (cryo-EM) structure of the human CLC-7/Ostm1 complex (HsCLC-7/Ostm1) at a resolution of 3.6 Å. Our structure reveals a new state of the CLC-7/Ostm1 heterotetramer, in which the cytoplasmic domain of CLC-7 is absent, likely due to high flexibility. The disordered cytoplasmic domain is probably not able to restrain CLC-7 subunits and thus allow their relative movements. The movements result in an approximately half smaller interface between the CLC-7 transmembrane domains than that in a previously reported CLC-7/Ostm1 structure with a well-folded cytoplasmic domain. Key interactions involving multiple osteopetrosis-related residues are affected by the interface change.

Active intracellular transport is mainly performed by a group of special nanomachines called motor proteins. During transport, cooperation between motor proteins significantly influences important transport features, such as distance and velocity. To understand this mechanism, we combine Gillespie simulation and analytical derivation to demonstrate how the mechanical properties of a single motor influence the cooperation between multiple motors, further regulating the transport distance. In addition, we build a deep learning model to help us quickly obtain the motor parameters. Our results shed light on the physical nature of intracellular transport by motor proteins with the same directionality.