The kinetics of Criegee intermediates are important for atmospheric modeling. However, the quantitative kinetics of Criegee intermediates are still very limited, especially for those with hydroxy groups. Here, we calculate rate constants for the unimolecular reaction of E-glycolaldehyde oxide [E-hydroxyethanal oxide, E-(CH₂OH)CHOO], for its reactions with H₂O and (H₂O)₂, and for the reaction of the E-(CH₂OH)CHOO…H₂O complex with H₂O. For the highest level of electronic structure, we use W3X-L//CCSD(T)-F12a/cc-pVDZ-F12 for the unimolecular reaction and the reaction with water and W3X-L//DF-CCSD(T)-F12b/jun-cc-pVDZ for the reaction with 2 water molecules. For the dynamics, we use a dual-level strategy that combines conventional transition state theory with the highest level of electronic structure and multistructural canonical variational transition state theory with small-curvature tunneling with a validated density functional for the electronic structure. This dynamical treatment includes high-frequency anharmonicity, torsional anharmonicity, recrossing effects, and tunneling. We find that the unimolecular reaction of E-(CH₂OH)CHOO depends on both temperature and pressure. The calculated results show that E-(CH₂OH)CHOO…H₂O + H₂O is the dominant entrance channel, while previous investigations only considered Criegee intermediates + (H₂O)₂. In addition, we find that the atmospheric lifetime of E-(CH₂OH)CHOO with respect to 2 water molecules is particularly short with a value of 1.71 × 10⁻⁶ s at 0 km, which is about 2 orders of magnitude shorter than those usually assumed for Criegee intermediate reactions with water dimer. We also find that the OH group in E-(CH₂OH)CHOO enhances its reactivity.

Detection in high fidelity of tipping points, the emergence of which is often induced by invisible changes in internal structures or/and external interferences, is paramountly beneficial to understanding and predicting complex dynamical systems (CDSs). Detection approaches, which have been fruitfully developed from several perspectives (e.g., statistics, dynamics, and machine learning), have their own advantages but still encounter difficulties in the face of high-dimensional, fluctuating datasets. Here, using the reservoir computing (RC), a recently notable, resource-conserving machine learning method for reconstructing and predicting CDSs, we articulate a model-free framework to accomplish the detection only using the time series observationally recorded from the underlying unknown CDSs. Specifically, we encode the information of the CDS in consecutive time durations of finite length into the weights of the readout layer in an RC, and then we use the learned weights as the dynamical features and establish a mapping from these features to the system’s changes. Our designed framework can not only efficiently detect the changing positions of the system but also accurately predict the intensity change as the intensity information is available in the training data. We demonstrate the efficacy of our supervised framework using the dataset produced by representative physical, biological, and real-world systems, showing that our framework outperforms those traditional methods on the short-term data produced by the time-varying or/and noise-perturbed systems. We believe that our framework, on one hand, complements the major functions of the notable RC intelligent machine and, on the other hand, becomes one of the indispensable methods for deciphering complex systems.

Effective condition monitoring and fault diagnosis of bearings can not only maximize the life of rolling bearings and prevent unexpected shutdowns caused by equipment failures but also eliminate unnecessary costs and waste caused by excessive maintenance. However, the existing deep-learning-based bearing fault diagnosis models have the following defects. First of all, these models have a large demand for fault data. Second, the previous models only consider that single-scale features are generally less effective in diagnosing bearing faults. Therefore, we designed a bearing fault data collection platform based on the Industrial Internet of Things, which is used to collect bearing status data from sensors in real time and feed it back into the diagnostic model. On the basis of this platform, we propose a bearing fault diagnosis model based on deep generative models with multiscale features (DGMMFs) to solve the above problems. The DGMMF model is a multiclassification model, which can directly output the abnormal type of the bearing. Specifically, the DGMMF model uses 4 different variational autoencoder models to augment the bearing data and integrates features of different scales. Compared with single-scale features, these multiscale features contain more information and can perform better. Finally, we conducted a large number of related experiments on the real bearing fault datasets and verified the effectiveness of the DGMMF model using multiple evaluation metrics. The DGMMF model has achieved the highest value under all metrics, among which the value of precision is 0.926, the value of recall is 0.924, the value of accuracy is 0.926, and the value of F1 score is 0.925.

The majority of atherothrombotic events (e.g., cerebral or myocardial infarction) often occur as a result of plaque rupture or erosion in the carotid, and thereby it is urgent to assess plaque vulnerability and predict adverse cerebrovascular events. However, the monitoring evolution from stable plaque into life-threatening high-risk plaque in the slender carotid artery is a great challenge, due to not enough spatial resolution for imaging the carotid artery based on most of reported fluorescent probes. Herein, copolymerizing with the small molecules of acceptor-donor-acceptor-donor-acceptor (A-D-A′-D-A) and the electron-donating units (D′), the screened second near-infrared (NIR-II) nanoprobe presents high quantum yield and good stability, so that it enables to image slender carotid vessel with enough spatial resolution. Encouragingly, NIR-II nanoprobe can effectively target to intraplaque macrophage, meanwhile distinguishing vulnerable plaque in carotid atherosclerosis in living mice. Moreover, the NIR-II nanoprobe can dynamically monitor the fresh bleeding spots in carotid plaque, indicating the increased risk of plaque instability. Besides, magnetic resonance imaging is integrated with NIR-II fluorescence imaging to provide contrast for subtle structure (e.g., narrow lumen and lipid pool), via incorporating ultrasmall superparamagnetic iron oxide into the NIR-II nanoprobe. Thus, such hybrid NIR-II/magnetic resonance imaging multimodal nanoprobe provides an effective tool for assessing carotid plaque burden, selecting high-risk plaque, and imaging intraplaque hemorrhage, which is promising for reducing cerebral/ myocardial infarction-associated morbidity and mortality.

Monocyte-to-M0/M1 macrophage differentiation with unclear molecular mechanisms is a pivotal cellular event in many cardiovascular diseases including atherosclerosis. Long non-coding RNAs (lncRNAs) are a group of protein expression regulators; however, the roles of monocyte-lncRNAs in macrophage differentiation and its related vascular diseases are still unclear. The study aims to investigate whether the novel leukocyte-specific lncRNA Morrbid could regulate macrophage differentiation and atherogenesis. We identified that Morrbid was increased in monocytes and arterial walls from atherosclerotic mouse and from patients with atherosclerosis. In cultured monocytes, Morrbid expression was markedly increased during monocyte to M0 macrophage differentiation with an additional increase during M0 macrophage-to-M1 macrophage differentiation. The differentiation stimuli-induced monocyte–macrophage differentiation and the macrophage activity were inhibited by Morrbid knockdown. Moreover, overexpression of Morrbid alone was sufficient to elicit the monocyte–macrophage differentiation. The role of Morrbid in monocyte–macrophage differentiation was also identified in vivo in atherosclerotic mice and was verified in Morrbid knockout mice. We identified that PI3-kinase/Akt was involved in the up-regulation of Morrbid expression, whereas s100a10 was involved in Morrbid-mediated effect on macrophage differentiation. To provide a proof of concept of Morrbid in pathogenesis of monocyte/macrophage-related vascular disease, we applied an acute atherosclerosis model in mice. The results revealed that overexpression of Morrbid enhanced but monocyte/macrophage-specific Morrbid knockout inhibited the monocytes/macrophages recruitment and atherosclerotic lesion formation in mice. The results suggest that Morrbid is a novel biomarker and a modulator of monocyte–macrophage phenotypes, which is involved in atherogenesis.

The therapeutic outcomes of conventional oral medications against ulcerative colitis (UC) are restricted by inefficient drug delivery to the colitis mucosa and weak capacity to modulate the inflammatory microenvironment. Herein, a fluorinated pluronic (FP127) was synthesized and employed to functionalize the surface of mulberry leaf-derived nanoparticles (MLNs) loading with resveratrol nanocrystals (RNs). The obtained FP127@RN-MLNs possessed exosome-like morphologies, desirable particle sizes (around 171.4 nm), and negatively charged surfaces (−14.8 mV). The introduction of FP127 to RN-MLNs greatly improved their stability in the colon and promoted their mucus infiltration and mucosal penetration capacities due to the unique fluorine effect. These MLNs could efficiently be internalized by colon epithelial cells and macrophages, reconstruct disrupted epithelial barriers, alleviate oxidative stress, provoke macrophage polarization to M2 phenotype, and down-regulate inflammatory responses. Importantly, in vivo studies based on chronic and acute UC mouse models demonstrated that oral administration of chitosan/alginate hydrogel-embedding FP127@RN-MLNs achieved substantially improved therapeutic efficacies compared with nonfluorinated MLNs and a first-line UC drug (dexamethasone), as evidenced by decreased colonic and systemic inflammation, integrated colonic tight junctions, and intestinal microbiota balance. This study brings new insights into the facile construction of a natural, versatile nanoplatform for oral treatment of UC without adverse effects.

Heterogeneous nucleation plays a critical role in the phase transition of water, which can cause damage in various systems. Here, we report that heterogeneous nucleation can be inhibited by utilizing hydrogel coatings to isolate solid surfaces and water. Hydrogels, which contain over 90% water when fully swelled, exhibit a high degree of similarity to water. Due to this similarity, there is a great energy barrier for heterogeneous nucleation along the water–hydrogel interface. Additionally, hydrogel coatings, which possess polymer networks, exhibit higher fracture energy and more robust adhesion to solid surfaces compared to water. This high fracture and adhesion energy acts as a deterrent for fracture nucleation within the hydrogel or along the hydrogel–solid interface. With a hydrogel layer approximately 100 μm thick, the boiling temperature of water under atmospheric pressure can be raised from 100 to 108 °C. Notably, hydrogel coatings also result in remarkable reductions in cavitation pressure on multiple solid surfaces. We have demonstrated the efficacy of hydrogel coatings in preventing damages resulting from acceleration-induced cavitation. Hydrogel coatings have the potential to alter the energy landscape of heterogeneous nucleation on the water–solid interface, making them an exciting avenue for innovation in heat transfer and fluidic systems.

The oxygen octahedral rotation (OOR) forms fundamental atomic distortions and symmetries in perovskite oxides and definitely determines their properties and functionalities. Therefore, epitaxial strain and interfacial structural coupling engineering have been developed to modulate the OOR patterns and explore novel properties, but it is difficult to distinguish the 2 mechanisms. Here, different symmetries are induced in Na_0.5Bi_0.5TiO₃ (NBT) epitaxial films by interfacial oxygen octahedral coupling rather than epitaxial strain. The NBT film grown on the Nb:SrTiO₃ substrate exhibits a paraelectric tetragonal phase, while with La_0.5Sr_0.5MnO₃ as a buffer layer, a monoclinic phase and robust ferroelectricity are obtained, with a remanent polarization of 42 μC cm⁻² and a breakdown strength of 7.89 MV cm⁻¹, which are the highest record among NBT-based films. Moreover, the interfacial oxygen octahedral coupling effect is demonstrated to propagate to the entire thickness of the film, suggesting an intriguing long-range effect. This work provides a deep insight into understanding the structure modulation in perovskite heterostructures and an important avenue for achieving unique functionalities.

Mitochondrial dysfunction is a hallmark feature of cellular senescence and organ aging. Here, we asked whether the mitochondrial antiviral signaling protein (MAVS), which is essential for driving antiviral response, also regulates human stem cell senescence. To answer this question, we used CRISPR/Cas9-mediated gene editing and directed differentiation techniques to generate various MAVS-knockout human stem cell models. We found that human mesenchymal stem cells (hMSCs) were sensitive to MAVS deficiency, as manifested by accelerated senescence phenotypes. We uncovered that the role of MAVS in maintaining mitochondrial structural integrity and functional homeostasis depends on its interaction with the guanosine triphosphatase optic atrophy type 1 (OPA1). Depletion of MAVS or OPA1 led to the dysfunction of mitochondria and cellular senescence, whereas replenishment of MAVS or OPA1 in MAVS-knockout hMSCs alleviated mitochondrial defects and premature senescence phenotypes. Taken together, our data underscore an uncanonical role of MAVS in safeguarding mitochondrial homeostasis and antagonizing human stem cell senescence.

With the progress of theoretical and applied technologies, the communication system based on the classical encryption is seriously threatened by quantum computing and distributed computing. A communication method that directly loads confidential information on the quantum state, quantum secure direct communication (QSDC), came into being for resisting security threats. Here, we report the first continuous-variable QSDC (CV-QSDC) experimental demonstration for verifying the feasibility and effectiveness of the CV-QSDC protocol based on Gaussian mapping and propose a parameter estimation for signal classification under the actual channels. In our experiment, we provided 4 × 10² blocks, where each block contains 10⁵ data for direct information transmission. For the transmission distance of 5 km in our experiment, the excess noise is 0.0035 SNU, where SNU represents the unit of shot-noise units. The 4.08 × 10⁵ bit per second experimental results firmly demonstrated the feasibility of CV-QSDC under the fiber channel. The proposed grading judgment method based on parameter estimation provides a practical and available message processing scheme for CV-QSDC in a practical fiber channel and lays the groundwork for the grading reconciliation.

Semiconducting polymers (SPs) have shown great feasibility as candidates for near-infrared-II (NIR-II) fluorescence imaging-navigated photothermal therapy due to their strong light-harvesting ability and flexible tunability. However, the fluorescence signal of traditional SPs tends to quench in their aggregate states owing to the strong π–π stacking, which can lead to the radiative decay pathway shutting down. To address this issue, aggregation-induced emission effect has been used as a rational tactic to boost the aggregate-state fluorescence of NIR-II emitters. In this contribution, we developed a precise molecular engineering tactic based on the block copolymerizations that integrate planar and twisted segments into one conjugated polymer backbone, providing great flexibility in tuning the photophysical properties and photothermal conversion capacity of SPs. Two monomers featured with twisted and planar architectures, respectively, were tactfully incorporated via a ternary copolymerization approach to produce a series of new SPs. The optimal copolymer (SP2) synchronously shows desirable absorption ability and good NIR-II quantum yield on the premise of maintaining typical aggregation-induced emission characteristics, resulting in balanced NIR-II fluorescence brightness and photothermal property. Water-dispersible nanoparticles fabricated from the optimal SP2 show efficient photothermal therapeutic effects both in vitro and in vivo. The in vivo investigation reveals the distinguished NIR-II fluorescence imaging performance of SP2 nanoparticles and their photothermal ablation toward tumor with prominent tumor accumulation ability and excellent biocompatibility.

Recent advancements in two-dimensional materials have shown huge potential for optoelectronic applications. It is challenging to achieve highly effective and sensitive broadband photodetection based on MoS₂ devices. Defect engineering, such as introducing vacancies, can narrow the bandgap and boost the separation of photogenerated carriers by defect states but leads to a slow response speed. Herein, we propose a nickel nanoparticle-induced gateless photogating effect with a unique energy band structure to enable the application of defect engineering and achieve high optoelectronic performance. The device based on Ni nanoparticle-decorated MoS₂ with S vacancies exhibited high responsivities of 106.21 and 1.38 A W⁻¹ and detectivities of 1.9 × 10¹² and 8.9 × 10⁹ Jones under 532 and 980 nm illumination (visible to near infrared), respectively, with highly accelerated response speed. This strategy provides new insight into optimizing defect engineering to design high-performance optoelectronic devices capable of broadband photodetection.

Wide-bandgap (WBG) perovskite solar cells (PSCs) are essential for highly efficient and stable silicon/perovskite tandem solar cells. In this study, we adopted a synthetic strategy with lead thiocyanate (Pb(SCN)₂) additive and methylammonium chloride (MACl) posttreatment to enhance the crystallinity and improve the interface of WBG perovskite films with a bandgap of 1.68 eV. The excessive PbI₂ was formed at grain boundaries and converted into MAPbI_3−xCl_x perovskites, which are utilized to form the graded heterojunction (GHJ) and compressive strain. This is beneficial for passivating nonradiative recombination defects, suppressing halide phase segregation, and facilitating carrier extraction. Subsequently, the device with GHJ delivered a champion efficiency of 20.30% and superior stability in ambient air and under 85 °C. Finally, we achieved a recorded efficiency of 30.91% for 4-terminal WBG perovskite/TOPCon tandem silicon solar cells. Our findings demonstrate a promising approach for fabricating efficient and stable WBG PSCs through the formation of GHJ.

Three-dimensional (3D) printing is emerging as a transformative technology for biomedical engineering. The 3D printed product can be patient-specific by allowing customizability and direct control of the architecture. The trial-and-error approach currently used for developing the composition of printable inks is time- and resource-consuming due to the increasing number of variables requiring expert knowledge. Artificial intelligence has the potential to reshape the ink development process by forming a predictive model for printability from experimental data. In this paper, we constructed machine learning (ML) algorithms including decision tree, random forest (RF), and deep learning (DL) to predict the printability of biomaterials. A total of 210 formulations including 16 different bioactive and smart materials and 4 solvents were 3D printed, and their printability was assessed. All ML methods were able to learn and predict the printability of a variety of inks based on their biomaterial formulations. In particular, the RF algorithm has achieved the highest accuracy (88.1%), precision (90.6%), and F1 score (87.0%), indicating the best overall performance out of the 3 algorithms, while DL has the highest recall (87.3%). Furthermore, the ML algorithms have predicted the printability window of biomaterials to guide the ink development. The printability map generated with DL has finer granularity than other algorithms. ML has proven to be an effective and novel strategy for developing biomaterial formulations with desired 3D printability for biomedical engineering applications.

The increasingly aging society led to a rise in the prevalence of chronic wounds (CWs), posing a significant burden to public health on a global scale. One of the key features of CWs is the presence of a maladjusted immune microenvironment characterized by persistent and excessive (hyper)inflammation. A variety of immunomodulatory therapies have been proposed to address this condition. Yet, to date, current delivery systems for immunomodulatory therapy remain inadequate and lack efficiency. This highlights the need for new therapeutic delivery systems, such as nanosystems, to manage the pathological inflammatory imbalance and, ultimately, improve the treatment outcomes of CWs. While a plethora of immunomodulatory nanosystems modifying the immune microenvironment of CWs have shown promising therapeutic effects, the literature on the intersection of immunomodulatory nanosystems and CWs remains relatively scarce. Therefore, this review aims to provide a comprehensive overview of the pathogenesis and characteristics of the immune microenvironment in CWs, discuss important advancements in our understanding of CW healing, and delineate the versatility and applicability of immunomodulatory nanosystems-based therapies in the therapeutic management of CWs. In addition, we herein also shed light on the main challenges and future perspectives in this rapidly evolving research field.

High-performance photodetectors hold promising potential in optical communication and imaging systems. However, conventional counterparts are suffering narrow detection range, high power consumption, and poor polarization sensitivity. Characteristics originating from switchable polarization in ferroelectrics can be used to optimize the photo-to-electric procedure and improve the photodetection performance. In this regard, we constructed a configuration by integrating 2-dimensional molybdenum disulfide (MoS₂) with ferroelectric lithium niobate (LiNbO₃), resulting in the MoS₂/LiNbO₃ heterostructured photodetector. Benefiting from the pyroelectric effect of LiNbO₃, the limitation of bandgap on the detection range can be broken, thus broadening the response band of the detector to 365 to 1,064 nm, as well as enabling the self-powered characteristic. Meanwhile, high carrier mobility and decent light absorbance of MoS₂ introduce robust light-matter interactions with the underlying LiNbO₃, leading to ultrafast rise/fall times of ≈150 μs/250 μs and switching ratios of up to ≈190. Moreover, the highest responsivity, specific detectivity, and external quantum efficiency achieved were 17.3 A·W⁻¹, 4.3 × 10¹¹ Jones, and 4,645.78%, respectively. Furthermore, because of the anisotropy of the spontaneous-polarized LiNbO₃ substrate, the photocurrent of the device achieved a dichroic ratio of 7.42, comparing favorably to most MoS₂-based photodetectors. This work demonstrates the integration potential between ferroelectric LiNbO₃ and 2-dimensional materials for high-performance photodetection.

Measuring the characteristics of seawater constituent is in great demand for studies of marine ecosystems and biogeochemistry. However, existing techniques based on remote sensing or in situ samplings present various tradeoffs with regard to the diversity, synchronism, temporal-spatial resolution, and depth-resolved capacity of their data products. Here, we demonstrate a novel oceanic triple-field-of-view (FOV) high-spectral-resolution lidar (HSRL) with an iterative retrieval approach. This technique provides, for the first time, comprehensive, continuous, and vertical measurements of seawater absorption coefficient, scattering coefficient, and slope of particle size distribution, which are validated by simulations and field experiments. Furthermore, it depicts valuable application potentials in the accuracy improvement of seawater classification and the continuous estimation of depth-resolved particulate organic carbon export. The triple-FOV HSRL with high performance could greatly increase the knowledge of seawater constituents and promote the understanding of marine ecosystems and biogeochemistry.

Flowing water can be used as an energy source for generators, providing a major part of the energy for daily life. However, water is rarely used for information or electronic devices. Herein, we present the feasibility of a polarized liquid-triggered photodetector in which polarized water is sandwiched between graphene and a semiconductor. Due to the polarization and depolarization processes of water molecules driven by photogenerated carriers, a photo-sensitive current can be repeatedly produced, resulting in a high-performance photodetector. The response wavelength of the photodetector can be fine-tuned as a result of the free choice of semiconductors as there is no requirement of lattice match between graphene and the semiconductors. Under zero voltage bias, the responsivity and specific detectivity of Gr/NaCl (0.5 M)W/N-GaN reach values of 130.7 mA/W and 2.3 × 10⁹ Jones under 350 nm illumination, respectively. Meanwhile, using a polar liquid photodetector can successfully read the photoplethysmography signals to produce accurate oxygen blood saturation and heart rate. Compared with the commercial pulse oximetry sensor, the average errors of oxygen saturation and heart rate in the designed photoplethysmography sensor are ~1.9% and ~2.1%, respectively. This study reveals that water can be used as a high-performance photodetector in informative industries.

Familial hypercholesterolemia (FH) is a frequently occurring genetic disorder that is linked to early-onset cardiovascular disease. If left untreated, patients with this condition can develop severe cardiovascular complications. Unfortunately, many patients remain undiagnosed, and even when diagnosed, the treatment is often not optimal. Although mutations in the LDLR gene are the primary cause of FH, predicting whether novel variants are pathogenic is not a straightforward task. Understanding the functionality of LDLR variants is crucial in uncovering the genetic basis of FH. Our study utilized CRISPR/Cas9 cytosine base editors in pooled screens to establish a novel approach for functionally assessing tens of thousands of LDLR variants on a large scale. A total of more than 100 single guide RNAs (sgRNAs) targeting LDLR pathogenic mutations were successfully screened with relatively high accuracy. Out of these, 5 sgRNAs were further subjected to functional verification studies, including 1 in the promoter, 1 in the antisense RNA, 1 in the exon, and 2 in the intron. Except for the variant caused by the sgRNA located at intron 16, the functionalities of the other LDLR variants were all downregulated. The high similarity of LDLR intron sequences may lead to some false positives. Overall, these results confirm the reliability of the large-scale screening strategy for functional analysis of LDLR variants, and the screened candidate pathogenic mutations could be used as an auxiliary means of clinical gene detection to prevent FH-induced heart disease.

Infectious diseases severely threaten public health and global biosafety. In addition to transmission through the air, pathogenic microorganisms have also been detected in environmental liquid samples, such as sewage water. Conventional biochemical detection methodologies are time-consuming and cost-ineffective, and their detection limits hinder early diagnosis. In the present study, ultrafine plasmonic fiber probes with a diameter of 125 μm are fabricated for clustered regularly interspaced short palindromic repeats/CRISPR-associated protein (CRISPR/Cas)-12a-mediated sensing of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Single-stranded DNA exposed on the fiber surface is trans-cleaved by the Cas12a enzyme to release gold nanoparticles that are immobilized onto the fiber surface, causing a sharp reduction in the surface plasmon resonance (SPR) wavelength. The proposed fiber probe is virus-specific with the limit of detection of ~2,300 copies/ml, and genomic copy numbers can be reflected as shifts in wavelengths. A total of 21 sewage water samples have been examined, and the data obtained are consistent with those of quantitative polymerase chain reaction (qPCR). In addition, the Omicron variant and its mutation sites have been fast detected using S gene-specific Cas12a. This study provides an accurate and convenient approach for the real-time surveillance of microbial contamination in sewage water.