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Atomic metal sites, such as Fe, Co, and Mn, coordinated with N and embedded in carbon, are the most promising platinum-group-metal (PGM)-free catalysts for acidic oxygen reduction reaction (ORR) in polymer electrolyte membrane fuel cells. Among others, Mn sites are preferable to minimize potential Fenton reactions within the electrode. Herein, we demonstrate a facile and scalable synthetic method to prepare atomic Mn-N-C catalysts by directly converting manganese oxides into active, stable MnN₄sites via solid-state reactions. Post-treatment with ammonium chloride can enhance the intrinsic ORR activity of MnN_xmoieties by introducing additional nitrogen groups and defects. Subsequent post-treatment with organic molecules, such as benzimidazole, promotes the formation of a robust carbon structure and significantly improves catalyst stability. The resulting Mn-N-C catalyst exhibits promising ORR activity, achieving a half-wave potential of 0.83 VvsRHE in aqueous 0.5 M H₂SO₄electrolyte, outperforming most PGM-free ORR catalysts. A corresponding membrane electrode assembly (MEA) generated a current density of ∼400 mA cm⁻²at 0.67 V and a peak power density of 0.46 W cm⁻². Significantly, post-treatment with benzimidazole improved catalyst stability, retaining 85% of the MEA performance, although the initial performance was compromised. 

The electronic energy level structure of yttrium monoxide (YO) provides a long-lived, low-lying ${}^2\mathrm{\Delta }$state ideal for high-precision molecular spectroscopy, narrowline laser cooling at the single photon-recoil limit, and studying dipolar physics with unprecedented interaction strength. High-resolution laser spectroscopy of ultracold laser-cooled YO molecules is used to study the Stark effect in the ${\mathrm{A}}^{\mathrm{\prime }}{}^2{\mathrm{\Delta }}_{3/2}J=3/2$state. An immediate onset of the linear Stark effect is observed in the presence of weak applied electric fields due to the near-degenerate $\mathrm{\Lambda }$doublet and the large electric dipole moment. By applying a small electric field the Stark-insensitive state is spectroscopically isolated and the absolute transition frequency to the $\mathrm{X}{}^2{\mathrm{\Sigma }}^{+}$electronic ground state is determined with a fractional frequency uncertainty of $9\times {10}^{-12}$. This electric field control is necessary to implement a quasi-closed photon-cycling scheme that preserves parity. With this scheme the first narrowline laser cooling of a molecule is demonstrated, reducing the temperature of sub-Doppler cooled YO in two dimensions. 

Abstract Clustered regularly interspaced short palindromic repeats (CRISPR) screening coupled with single-cell RNA sequencing has emerged as a powerful tool to characterize the effects of genetic perturbations on the whole transcriptome at a single-cell level. However, due to its sparsity and complex structure, analysis of single-cell CRISPR screening data is challenging. In particular, standard differential expression analysis methods are often underpowered to detect genes affected by CRISPR perturbations. We developed a statistical method for such data, called guided sparse factor analysis (GSFA). GSFA infers latent factors that represent coregulated genes or gene modules; by borrowing information from these factors, it infers the effects of genetic perturbations on individual genes. We demonstrated through extensive simulation studies that GSFA detects perturbation effects with much higher power than state-of-the-art methods. Using single-cell CRISPR data from human CD8⁺T cells and neural progenitor cells, we showed that GSFA identified biologically relevant gene modules and specific genes affected by CRISPR perturbations, many of which were missed by existing methods, providing new insights into the functions of genes involved in T cell activation and neurodevelopment. 

Abstract Many existing pipelines for scRNA-seq data apply pre-processing steps such as normalization or imputation to account for excessive zeros or “drop-outs. Here, we extensively analyze diverse UMI data sets to show that clustering should be the foremost step of the workflow. We observe that most drop-outs disappear once cell-type heterogeneity is resolved, while imputing or normalizing heterogeneous data can introduce unwanted noise. We propose a novel framework HIPPO (Heterogeneity-Inspired Pre-Processing tOol) that leverages zero proportions to explain cellular heterogeneity and integrates feature selection with iterative clustering. HIPPO leads to downstream analysis with greater flexibility and interpretability compared to alternatives.