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Abstract Two-dimensional (2D) ferroelectric and magnetic van der Waals materials are emerging platforms for the discovery of novel cooperative quantum phenomena and development of energy-efficient logic and memory applications as well as neuromorphic and topological computing. This review presents a comprehensive survey of the rapidly growing 2D ferroic family from the synthesis perspective, including brief introductions to the top-down and bottom-up approaches for fabricating 2D ferroic flakes, thin films, and heterostructures as well as the important characterization techniques for assessing the sample properties. We also discuss the key challenges and future directions in the field, including scalable growth, property control, sample stability, and integration with other functional materials. 

Abstract Colorectal cancer (CRC) cells display remarkable adaptability, orchestrating metabolic changes that confer growth advantages, pro‐tumor microenvironment, and therapeutic resistance. One such metabolic change occurs in glutamine metabolism. Colorectal tumors with high glutaminase (GLS) expression exhibited reduced T cell infiltration and cytotoxicity, leading to poor clinical outcomes. However, depletion of GLS in CRC cells has minimal effect on tumor growth in immunocompromised mice. By contrast, remarkable inhibition of tumor growth is observed in immunocompetent mice when GLS is knocked down. It is found that GLS knockdown in CRC cells enhanced the cytotoxicity of tumor‐specific T cells. Furthermore, the single‐cell flux estimation analysis (scFEA) of glutamine metabolism revealed that glutamate‐to‐glutathione (Glu‐GSH) flux, downstream of GLS, rather than Glu‐to‐2‐oxoglutarate flux plays a key role in regulating the immune response of CRC cells in the tumor. Mechanistically, inhibition of the Glu‐GSH flux activated reactive oxygen species (ROS)‐related signaling pathways in tumor cells, thereby increasing the tumor immunogenicity by promoting the activity of the immunoproteasome. The combinatorial therapy of Glu‐GSH flux inhibitor and anti‐PD‐1 antibody exhibited a superior tumor growth inhibitory effect compared to either monotherapy. Taken together, the study provides the first evidence pointing to Glu‐GSH flux as a potential therapeutic target for CRC immunotherapy. 

Boolean matrix factorization (BMF) has been widely utilized in fields such as recommendation systems, graph learning, text mining, and -omics data analysis. Traditional BMF methods decompose a binary matrix into the Boolean product of two lower-rank Boolean matrices plus homoscedastic random errors. However, real-world binary data typically involves biases arising from heterogeneous row- and column-wise signal distributions. Such biases can lead to suboptimal fitting and unexplainable predictions if not accounted for. In this study, we reconceptualize the binary data generation as the Boolean sum of three components: a binary pattern matrix, a background bias matrix influenced by heterogeneous row or column distributions, and random flipping errors. We introduce a novel Disentangled Representation Learning for Binary matrices (DRLB) method, which employs a dual auto-encoder network to reveal the true patterns. DRLB can be seamlessly integrated with existing BMF techniques to facilitate bias-aware BMF. Our experiments with both synthetic and real-world datasets show that DRLB significantly enhances the precision of traditional BMF methods while offering high scalability. Moreover, the bias matrix detected by DRLB accurately reflects the inherent biases in synthetic data, and the patterns identified in the bias-corrected real-world data exhibit enhanced interpretability. 

Glyceric acid [HOCH₂CH(OH)COOH]—the simplest sugar acid—represents a key molecule in biochemical processes vital for metabolism in living organisms such as glycolysis. Although critically linked to the origins of life and identified in carbonaceous meteorites with abundances comparable to amino acids, the underlying mechanisms of its formation have remained elusive. Here, we report the very first abiotic synthesis of racemic glyceric acid via the barrierless radical-radical reaction of the hydroxycarbonyl radical (HOĊO) with 1,2-dihydroxyethyl (HOĊHCH₂OH) radical in low-temperature carbon dioxide (CO₂) and ethylene glycol (HOCH₂CH₂OH) ices. Using isomer-selective vacuum ultraviolet photoionization reflectron time-of-flight mass spectrometry, glyceric acid was identified in the gas phase based on the adiabatic ionization energies and isotopic substitution studies. This work reveals the key reaction pathways for glyceric acid synthesis through nonequilibrium reactions from ubiquitous precursor molecules, advancing our fundamental knowledge of the formation pathways of key biorelevant organics—sugar acids—in deep space. 

Boolean matrix factorization (BMF) has been widely utilized in fields such as recommendation systems, graph learning, text mining, and -omics data analysis. Traditional BMF methods decompose a binary matrix into the Boolean product of two lower-rank Boolean matrices plus homoscedastic random errors. However, real-world binary data typically involves biases arising from heterogeneous row- and column-wise signal distributions. Such biases can lead to suboptimal fitting and unexplainable predictions if not accounted for. In this study, we reconceptualize the binary data generation as the Boolean sum of three components: a binary pattern matrix, a background bias matrix influenced by heterogeneous row or column distributions, and random flipping errors. We introduce a novel Disentangled Representation Learning for Binary matrices (DRLB) method, which employs a dual auto-encoder network to reveal the true patterns. DRLB can be seamlessly integrated with existing BMF techniques to facilitate bias-aware BMF. Our experiments with both synthetic and real-world datasets show that DRLB significantly enhances the precision of traditional BMF methods while offering high scalability. Moreover, the bias matrix detected by DRLB accurately reflects the inherent biases in synthetic data, and the patterns identified in the bias-corrected real-world data exhibit enhanced interpretability. 

Abstract Enols—tautomers of ketones or aldehydes—are considered key intermediates in the formation of prebiotic sugars and sugar acids. Although laboratory simulation experiments suggest that enols should be ubiquitous in the interstellar medium, the underlying formation mechanisms of enols in interstellar environments are largely elusive. Here, we present the laboratory experiments on the formation of glyoxal (HCOCHO) along with its ynol tautomer acetylenediol (HOCCOH) in interstellar ice analogs composed of carbon monoxide (CO) and water (H₂O) upon exposure to energetic electrons as a proxy for secondary electrons generated from Galactic cosmic rays. Utilizing tunable vacuum ultraviolet photoionization reflectron time-of-flight mass spectrometry, glyoxal and acetylenediol were detected in the gas phase during temperature-programmed desorption. Our results reveal the formation pathways of glyoxal via radical–radical recombination of two formyl (HĊO) radicals, and that of acetylenediol via keto-enol-ynol tautomerization. Due to the abundance of carbon monoxide and water in interstellar ices, glyoxal and acetylenediol are suitable candidates for future astronomical searches. Furthermore, the detection of acetylenediol in astrophysically relevant ices advances our understanding for the formation pathways of high-energy tautomers such as enols in deep space. 

Abstract In the last several years, there has been a surge in the development of machine learning potential (MLP) models for describing molecular systems. We are interested in a particular area of this field — the training of system‐specific MLPs for reactive systems — with the goal of using these MLPs to accelerate free energy simulations of chemical and enzyme reactions. To help new members in our labs become familiar with the basic techniques, we have put together a self‐guided Colab tutorial (https://cc-ats.github.io/mlp_tutorial/), which we expect to be also useful to other young researchers in the community. Our tutorial begins with the introduction of simple feedforward neural network (FNN) and kernel‐based (using Gaussian process regression, GPR) models by fitting the two‐dimensional Müller‐Brown potential. Subsequently, two simple descriptors are presented for extracting features of molecular systems: symmetry functions (including the ANI variant) and embedding neural networks (such as DeepPot‐SE). Lastly, these features will be fed into FNN and GPR models to reproduce the energies and forces for the molecular configurations in a Claisen rearrangement reaction.