Ultraphotostable Phosphorescent Nanosensors for Sensing the Lysosomal pH at the Single-Cell Level over Long Durations.

Ultraphotostable phosphorescent nanosensors have been designed for continuously sensing the lysosome pH over a long duration. The nanosensors exhibited excellent photostability, high accuracy, and capability to measure pH values during cell proliferation for up to 7 days. By arranging a metal-ligand complex of long phosphorescence lifetime and pH indicator in silica nanoparticles, we discover efficient Förster resonance energy transfer, which converts the pH-responsive UV-vis absorption signal of the pH indicator into a phosphorescent signal. Both the phosphorescent intensity and lifetime change at different pH values, and intracellular pH values can be accurately measured by our custom-built rapid phosphorescent lifetime imaging microscopy. The excellent photostability, high accuracy, and good biocompatibility prove that these nanosensors are a useful tool for tracing the fluctuation of pH values during endocytosis. The methodology can be easily adapted to design new nanosensors with different pKa or for different kinds of intracellular ions, as there are hundreds of pH and ion indicators readily available.

APEX-pHLA: A novel method for accurate prediction of the binding between exogenous short peptides and HLA class I molecules.

Human leukocyte antigen (HLA) molecules play critically significant role within the realm of immunotherapy due to their capacities to recognize and bind exogenous antigens such as peptides, subsequently delivering them to immune cells. Predicting the binding between peptides and HLA molecules (pHLA) can expedite the screening of immunogenic peptides and facilitate vaccine design. However, traditional experimental methods are time-consuming and inefficient. In this study, an efficient method based on deep learning was developed for predicting peptide-HLA binding, which treated peptide sequences as linguistic entities. It combined the architectures of textCNN and BiLSTM to create a deep neural network model called APEX-pHLA. This model operated without limitations related to HLA class I allele variants and peptide segment lengths, enabling efficient encoding of sequence features for both HLA and peptide segments. On the independent test set, the model achieved Accuracy, ROC_AUC, F1, and MCC is 0.9449, 0.9850, 0.9453, and 0.8899, respectively. Similarly, on an external test set, the results were 0.9803, 0.9574, 0.8835, and 0.7863, respectively. These findings outperformed fifteen methods previously reported in the literature. The accurate prediction capability of the APEX-pHLA model in peptide-HLA binding might provide valuable insights for future HLA vaccine design.

Serovar and sequence type distribution and phenotypic and genotypic antimicrobial resistance of Salmonella originating from pet animals in Chongqing, China.

A total of 334 Salmonella isolates were recovered from 6,223 pet rectal samples collected at 50 pet clinics, 42 pet shops, 7 residential areas, and 4 plazas. Forty serovars were identified that included all strains except for one isolate that did not cluster via self-agglutination, with Salmonella Typhimurium monophasic variant, Salmonella Kentucky, Salmonella Enteritidis, Salmonella Pomona, and Salmonella Give being the predominant serovars. Fifty-one sequence types were identified among the isolates, and ST198, ST11, ST19, ST451, ST34, and ST155 were the most common. The top four dominant antimicrobials to which isolates were resistant were sulfisoxazole, ampicillin, doxycycline, and tetracycline, and 217 isolates exhibited multidrug resistance. The prevalence of ß-lactamase genes in Salmonella isolates was 59.6%, and among these isolates, 185 harbored blaTEM, followed by blaCTX-M (66) and blaOXA (10). Moreover, six PMQR genes, namely, including qnrA (4.8%), qnrB (4.2%), qnrD (0.9%), qnrS (18.9%), aac(6')-Ib-cr (16.5%), and oqxB (1.5%), were detected. QRDR mutations (76.6%) were very common in Salmonella isolates, with the most frequent mutation in parC (T57S) (47.3%). Furthermore, we detected six tetracycline resistance genes in 176 isolates, namely, tet(A) (39.5%), tet(B) (8.1%), tet(M) (7.7%), tet(D) (5.4%), tet(J) (3.3%), and tet(C) (1.8%), and three sulfonamide resistance genes in 303 isolates, namely, sul1 (84.4%), sul2 (31.1%), and sul3 (4.2%). Finally, we found 86 isolates simultaneously harboring four types of resistance genes that cotransferred 2-7 resistance genes to recipient bacteria. The frequent occurrence of antimicrobial resistance, particularly in dogs and cats, suggests that antibiotic misuse may be driving multidrug-resistant Salmonella among pets.IMPORTANCEPet-associated human salmonellosis has been reported for many years, and antimicrobial resistance in pet-associated Salmonella has become a serious public health problem and has attracted increasing attention. There are no reports of Salmonella from pets and their antimicrobial resistance in Chongqing, China. In this study, we investigated the prevalence, serovar diversity, sequence types, and antimicrobial resistance of Salmonella strains isolated from pet fecal samples in Chongqing. In addition, ß-lactamase, QRDR, PMQR, tetracycline and sulfonamide resistance genes, and mutations in QRDRs in Salmonella isolates were examined. Our findings demonstrated the diversity of serovars and sequence types of Salmonella isolates. The isolates were widely resistant to antimicrobials, notably with a high proportion of multidrug-resistant strains, which highlights the potential direct or indirect transmission of multidrug-resistant Salmonella from pets to humans. Furthermore, resistance genes were widely prevalent in the isolates, and most of the resistance genes were spread horizontally between strains.

Achievement of waste sludge deep dehydration under acidic conditions with polydimethyldiallylammonium chloride and ferric chloride: performance and mechanism.

The biological treatment of wastewater generates a substantial amount of waste sludge that requires dewatering before final disposal. Efficient sludge dewatering is essential to minimize storage and transportation costs. In this study, the sludge conditioners polydimethyldiallylammonium chloride (PDMDAAC) and ferric chloride (FeCl3) were sequentially dosed, and the pH was adjusted to 3. As a result, the sludge moisture content (MC) was reduced to 59.4%, achieving deep dewatering. After conditioning, the tightly bound extracellular polymeric substances (TB-EPS) were reduced from 34.5 to 10.2 mg g-1 VSS, with the majority of the reduced fractions being composed of protein (PN). In contrast, soluble EPS increased more than 8 times. Subsequent studies revealed that the decrease in PN from TB-EPS primarily involved tryptophan and tyrosine proteins, accompanied by a significant reduction in the N-H and C[double bond, length as m-dash]C absorption peaks. These results highlight the critical role of TB-EPS dissolution in achieving deep dehydration, with the N-H in PN was identified as the key group influencing sludge dewatering. Combined with the changes in sludge particle size and morphology, the dewatering mechanism can be summarized as follows: PDMDAAC dissolves TB-EPS, simultaneously disrupting the floc structure and refining the sludge. Subsequently, FeCl3 reconstructs these elements, forming larger particle sizes. Finally, hydrochloric acid reduces TB-EPS once again, releasing bound water. This study offers alternative methods and new insights for achieving deep dewatering of waste sludge.

Whole-soil warming leads to substantial soil carbon emission in an alpine grassland.

The sensitivity of soil organic carbon (SOC) decomposition in seasonally frozen soils, such as alpine ecosystems, to climate warming is a major uncertainty in global carbon cycling. Here we measure soil CO2 emission during four years (2018-2021) from the whole-soil warming experiment (4 °C for the top 1 m) in an alpine grassland ecosystem. We find that whole-soil warming stimulates total and SOC-derived CO2 efflux by 26% and 37%, respectively, but has a minor effect on root-derived CO2 efflux. Moreover, experimental warming only promotes total soil CO2 efflux by 7-8% on average in the meta-analysis across all grasslands or alpine grasslands globally (none of these experiments were whole-soil warming). We show that whole-soil warming has a much stronger effect on soil carbon emission in the alpine grassland ecosystem than what was reported in previous warming experiments, most of which only heat surface soils.

Exploring the Conformational Landscape of Poly(l-lysine) Dendrimers Using Ion Mobility Mass Spectrometry.

Ion mobility mass spectrometry (IM-MS) measures the mass, size, and shape of ions in the same experiment, and structural information is provided via collision cross-section (CCS) values. The majority of commercially available IM-MS instrumentation relies on the use of CCS calibrants, and here, we present data from a family of poly(l-lysine) dendrimers and explore their suitability for this purpose. In order to test these compounds, we employed three different IM-MS platforms (Agilent 6560 IM-QToF, Waters Synapt G2, and a home-built variable temperature drift tube IM-MS) and used them to investigate six different generations of dendrimers in two buffer gases (helium and nitrogen). Each molecule gives a highly discrete CCS distribution suggestive of single conformers for each m/z value. The DTCCSN2 values of this series of molecules (molecular weight: 330-16,214 Da) range from 182 to 2941 Å2, which spans the CCS range that would be found by many synthetic molecules including supramolecular compounds and many biopolymers. The CCS values for each charge state were highly reproducible in day-to-day analysis on each instrument, although we found small variations in the absolute CCS values between instruments. The rigidity of each dendrimer was probed using collisionally activated and high-temperature IM-MS experiments, where no evidence for a significant CCS change ensued. Taken together, this data indicates that these polymers are candidates for CCS calibration and could also help to reconcile differences found in CCS measurements on different instrument geometries.

Inward motion of diamond nanoparticles inside an iron crystal.

In the absence of externally applied mechanical loading, it would seem counterintuitive that a solid particle sitting on the surface of another solid could not only sink into the latter, but also continue its rigid-body motion towards the interior, reaching a depth as distant as thousands of times the particle diameter. Here, we demonstrate such a case using in situ microscopic as well as bulk experiments, in which diamond nanoparticles ~100 nm in size move into iron up to millimeter depth, at a temperature about half of the melting point of iron. Each diamond nanoparticle is nudged as a whole, in a displacive motion towards the iron interior, due to a local stress induced by the accumulation of iron atoms diffusing around the particle via a short and easy interfacial channel. Our discovery underscores an unusual mass transport mode in solids, in addition to the familiar diffusion of individual atoms.

Protaetia brevitarsis larvae produce frass that can be used as an additive to immobilize Cd and improve fertility in alkaline soils.

Bioconversion of agricultural waste by Protaetia brevitarsis larvae (PBL) holds significant promise for producing high-quality frass organic amendments. However, the effects and mechanisms of PBL frass on Cd immobilization in an alkaline environment remain poorly understood. In this study, three types of frass, namely maize straw frass (MF), rice straw frass (RF), and sawdust frass (SF), were produced by feeding PBL. The Cd immobilization efficiencies of three frass in alkaline solutions and soils were investigated through batch sorption and incubation experiments, and spectroscopic techniques were employed to elucidate the sorption mechanisms of Cd onto different frass at the molecular level. The results showed that MF proved to be an efficient sorbent for Cd in alkaline solutions (176.67-227.27 mg g-1). X-ray absorption near-edge structure (XANES) spectroscopy indicated that Cd immobilization in frass is primarily attributed to the association with organic matter (OM-Cd, 78-90%). And MF had more oxygen-containing functional groups than the other frass. In weakly alkaline soils, MF application (0.5-1.5%) significantly decreased Cd bioavailability (5.65-18.48%) and concurrently improved soil nutrients (2.21-56.79%). Redundancy analysis (RDA) unveiled that pH, CEC, and available P were important factors controlling Cd fractions. Path analysis demonstrated that MF application affected Cd bioavailability directly and indirectly by influencing soil chemical properties and nutrients. In summary, MF, the product of PBL-mediated conversion maize straw, demonstrated promise as an effective organic amendment for Cd immobilization and fertility improvement in alkaline soils.

Ergodic criterion of a random diffusivity model.

The random diffusivity, initially proposed to explain Brownian yet non-Gaussian diffusion, has garnered significant attention due to its capacity not only for elucidating the internal physical mechanism of non-Gaussian diffusion, but also for establishing an analytical framework to characterize particle motion in complex environments. In this paper, based on the correlation function C(t_{1},t_{2})=〈D(t_{1})D(t_{2})〉 of random diffusivity D(t), we quantitatively propose a general criterion of determining the ergodic property of the Langevin equation with the arbitrary random diffusivity D(t). Due to the critical role of correlation function C(t_{1},t_{2}), we derive the criterion for the two cases with stationary diffusivity or nonstationary diffusivity, respectively. By utilizing the quantitative criterion, we can directly judge the ergodic properties of the random diffusivity model based on the correlation function C(t_{1},t_{2}) of random diffusivity D(t). Several typical diffusivities, including the common square of the Brownian motion and of the (fractional) Ornstein-Uhlenbeck process, are found to contribute to different ergodic properties, which validates our proposed criterion built on the correlation function C(t_{1},t_{2}).

Differentiating the impact of fine and coarse particulate matter on cause-specific cerebrovascular mortality: An individual-level, case-crossover study.

BACKGROUND AND OBJECTIVES: Many studies suggested that short-term exposure to fine particulate matter (PM2.5) and coarse particulate matter (PM2.5-10) was linked to elevated risk of cerebrovascular disease. However, little is known about the potentially differential effects of PM2.5 and PM2.5-10 on various types of cerebrovascular disease. METHODS: We collected individual cerebrovascular death records for all residents in Shanghai, China from 2005 to 2021. Residential daily air pollution data were predicted from a satellite model. The associations between particulate matters (PM) and cerebrovascular mortality were investigated by an individual-level, time-stratified, case-crossover design. The data was analyzed by the conditional logistic regression combined with the distributed lag model with a maximum lag of 7 days. Furthermore, we explored the effect modifications by sex, age and season. RESULTS: A total of 388,823 cerebrovascular deaths were included. Monotonous increases were observed for mortality of all cerebrovascular diseases except for hemorrhagic stroke. A 10 µg/m3 rise in PM2.5 was related to rises of 1.35% [95% confidence interval (CI): 1.04%, 1.66%] in mortality of all cerebrovascular diseases, 1.84% (95% CI: 1.25%, 2.44%) in ischemic stroke, 1.53% (95% CI: 1.07%, 1.99%) in cerebrovascular sequelae and 1.56% (95% CI: 1.08%, 2.05%) in ischemic stroke sequelae. The excess risk estimates per each 10 µg/m3 rise in PM2.5-10 were 1.47% (95% CI: 1.10%, 1.84%), 1.53% (95% CI: 0.83%, 2.24%), 1.93% (95% CI: 1.38%, 2.49%) and 2.22% (95% CI: 1.64%, 2.81%), respectively. The associations of both pollutants with all cerebrovascular outcomes were robust after controlling for co-pollutants. The associations were greater in females, individuals > 80 years, and during the warm season. CONCLUSIONS: Short-term exposures to both PM2.5 and PM2.5-10 may independently increase the mortality risk of cerebrovascular diseases, particularly of ischemic stroke and stroke sequelae.

Facial vascular visualization enhancement based on optical detection technology.

OBJECTIVE: This study aims to develop a facial vascular enhancement imaging system and analyze vascular distribution in the facial region to assess its potential in preventing unintended intravascular injections during cosmetic facial filling procedures. METHODS: A facial vascular enhancement imaging system based on optical detection technology was designed, and volunteers were recruited. The system was utilized to detect and analyze vascular distribution in various anatomical regions of the faces. The vascular visualization-enhanced images generated by the system were compared with visible light images to validate the vascular visualization capability of the system. Additionally, the reliability of vascular visualization was assessed by comparing the observed vascular patterns in the vascular visualization-enhanced images with those in near-infrared light images. RESULTS: Thirty volunteers were recruited. The vascular visualization-enhanced images produced by the system demonstrated a significant capacity to identify vascular morphology and yielded a higher vessel count compared to visible light images, particularly in the frontal, orbital, perioral, mental, temporal, cheek, and parotid masseter regions (p < 0.05). The temporal region exhibited the highest vascular density, followed by the cheek region and then the frontal region. Reliability analysis of vascular visualization enhancement indicated that the system's imaging of facial vasculature not only demonstrated reliability but also enhanced physicians' visual perception. CONCLUSION: Blood vessel distribution varies across facial regions. The facial vascular enhancement imaging system facilitates real-time and clear visualization of facial vasculature, offering immediate visual feedback to surgeons. This innovation holds promise for enhancing the safety and effectiveness of facial filling procedures.

3D-Printed Piezoelectric Stents for Electricity Generation Driven by Pressure Fluctuation.

Vascular stenting is a common procedure used to treat diseased blood vessels by opening the narrowed vessel lumen and restoring blood flow to ischemic tissues in the heart and other organs. In this work, we report a novel piezoelectric stent featuring a zigzag shape fabricated by fused deposition modeling three-dimensional (3D) printing with a built-in electric field. The piezoelectric composite was made of potassium sodium niobite microparticles and poly(vinylidene fluoride-co-hexafluoropropylene), complementing each other with good piezoelectric performance and mechanical resilience. The in situ poling yielded an appreciable piezoelectricity (d33 ∼ 4.2 pC N-1) of the as-printed stents. In vitro testing revealed that materials are nontoxic to vascular cells and have low thrombotic potential. Under stimulated blood pressure fluctuation, the as-printed piezoelectric stent was able to generate peak-to-peak voltage from 0.07 to 0.15 V corresponding to pressure changes from 20 to 120 Psi, giving a sensitivity of 7.02 × 10-4 V Psi-1. Biocompatible piezoelectric stents bring potential opportunities for the real-time monitoring of blood vessels or enabling therapeutic functions.

Structure and function of an intermediate GPCR-Gαßγ complex.

Unraveling the signaling roles of intermediate complexes is pivotal for G protein-coupled receptor (GPCR) drug development. Despite hundreds of GPCR-Gαßγ structures, these snapshots primarily capture the fully activated end-state complex. Consequently, a comprehensive understanding of the conformational transitions during GPCR activation and the roles of intermediate GPCR-G protein complexes in signaling remain elusive. Guided by a conformational landscape profiled by 19 F quantitative NMR ( 19 F-qNMR) and Molecular Dynamics (MD) simulations, we resolved the structure of an unliganded GPCR-G protein intermediate complex by blocking its transition to the fully activated end-state complex. More importantly, we presented direct evidence that the intermediate GPCR-Gαsßγ complex initiates a rate-limited nucleotide exchange without progressing to the fully activated end-state complex, thereby bridging a significant gap in our understanding the complexity of GPCR signaling. Understanding the roles of individual conformational states and their complexes in signaling efficacy and bias will help us to design drugs that discriminately target a disease-related conformation.

Oroxin A from Oroxylum indicum improves disordered lipid metabolism by inhibiting SREBPs in oleic acid-induced HepG2 cells and high-fat diet-fed non-insulin-resistant rats.

Background: Lipid metabolism disorders have become a major global public health issue. Due to the complexity of these diseases, additional research and drugs are needed. Oroxin A, the major component of Oroxylum indicum (L.) Kurz (Bignoniaceae), can improve the lipid profiles of diabetic and insulin-resistant (IR) rats. Because insulin resistance is strongly correlated with lipid metabolism, improving insulin resistance may also constitute an effective strategy for improving lipid metabolism. Thus, additional research on the efficacy and mechanism of oroxin An under non-IR conditions is needed. Methods: In this study, we established lipid metabolism disorder model rats by high-fat diet feeding and fatty HepG2 cell lines by treatment with oleic acid and evaluated the therapeutic effect and mechanism of oroxin A in vitro and in vivo through biochemical indicator analysis, pathological staining, immunoblotting, and immunofluorescence staining. Results: Oroxin A improved disordered lipid metabolism under non-IR conditions, improved the plasma and hepatic lipid profiles, and enhanced the lipid-lowering action of atorvastatin. Additionally, oroxin A reduced the total triglyceride (TG) levels by inhibiting sterol regulatory element-binding protein 1 (SREBP1) expression and reducing the expression of acetyl coenzyme A carboxylase (ACC) and fatty acid synthase (FASN) in vivo and in vitro. Oroxin A also reduced the total cholesterol (TC) levels by inhibiting SREBP2 expression and reducing HMGCR expression in vivo and in vitro. In addition, oroxin A bound to low-density lipoprotein receptor (LDLR) and increased AMPK phosphorylation. Conclusions: Our results suggested that oroxin A may modulate the nuclear transcriptional activity of SREBPs by binding to LDLR proteins and increasing AMPK phosphorylation. Oroxin A may thus reduce lipid synthesis and could be used for the treatment and prevention of lipid metabolism disorders.

Chemical Space Profiling of SARS-CoV-2 PLpro Using DNA-Encoded Focused Libraries.

DNA-encoded library (DEL) technology is gaining attention for its rapid construction and deconvolution capabilities. Our study explored a novel strategy using rational DELs tailored for the SARS-CoV-2 papain-like protease, which revealed new fragments. Structural changes post-DEL screening mimic traditional medicinal chemistry lead optimization. We unveiled unique aromatic structures offering an alternative optimization path. Notably, we identified superior binding fragments targeting the BL2 groove. Derivative 16 emerged as the most promising by exhibiting IC50 values of 0.25 µM. Derivative 6, which features an aromatic fragment capped with a naphthalene moiety, showed IC50 values of 2.91 µM. Molecular modeling revealed hydrogen bond interactions with Lys157 residue and potential covalent interactions with nearby amino acid residues. This research underscored DEL's potential for fragment-based drug discovery against SARS-CoV-2 protease.

Dynamic pathway linking Pakistan flooding to East Asian heatwaves.

In July to August 2022, Pakistan suffered historic flooding while record-breaking heatwaves swept southern China, causing severe socioeconomic impacts. Similar extreme events have frequently coincided between two regions during the past 44 years, but the underlying mechanisms remain unclear. Using observations and a suite of model experiments, here, we show that the upper-tropospheric divergent wind induced by convective heating over Pakistan excites a barotropic anomalous anticyclone over eastern China, which further leads to persistent heatwaves. Atmospheric model ensemble simulation indicates that this dynamic pathway linking Pakistan flooding and East Asian heatwaves is intrinsic to the climate system, largely independent of global sea surface temperature forcing. This dynamic connection is most active during July to August when convective variability is large over Pakistan and the associated divergent flow excites barotropic Rossby waves that propagate eastward along the upper troposphere westerly waveguide. This robust waveguide and the time delay offer hopes for improved subseasonal prediction of extreme events in East Asia.

A CT-based integrated model for preoperative prediction of occult lymph node metastasis in early tongue cancer.

Background: Occult lymph node metastasis (OLNM) is an essential prognostic factor for early-stage tongue cancer (cT1-2N0M0) and a determinant of treatment decisions. Therefore, accurate prediction of OLNM can significantly impact the clinical management and outcomes of patients with tongue cancer. The aim of this study was to develop and validate a multiomics-based model to predict OLNM in patients with early-stage tongue cancer. Methods: The data of 125 patients diagnosed with early-stage tongue cancer (cT1-2N0M0) who underwent primary surgical treatment and elective neck dissection were retrospectively analyzed. A total of 100 patients were randomly assigned to the training set and 25 to the test set. The preoperative contrast-enhanced computed tomography (CT) and clinical data on these patients were collected. Radiomics features were extracted from the primary tumor as the region of interest (ROI) on CT images, and correlation analysis and the least absolute shrinkage and selection operator (LASSO) method were used to identify the most relevant features. A support vector machine (SVM) classifier was constructed and compared with other machine learning algorithms. With the same method, a clinical model was built and the peri-tumoral and intra-tumoral images were selected as the input for the deep learning model. The stacking ensemble technique was used to combine the multiple models. The predictive performance of the integrated model was evaluated for accuracy, sensitivity, specificity, and the area under the receiver operating characteristic curve (AUC-ROC), and compared with expert assessment. Internal validation was performed using a stratified five-fold cross-validation approach. Results: Of the 125 patients, 41 (32.8%) showed OLNM on postoperative pathological examination. The integrated model achieved higher predictive performance compared with the individual models, with an accuracy of 84%, a sensitivity of 100%, a specificity of 76.5%, and an AUC-ROC of 0.949 (95% CI [0.870-1.000]). In addition, the performance of the integrated model surpassed that of younger doctors and was comparable to the evaluation of experienced doctors. Conclusions: The multiomics-based model can accurately predict OLNM in patients with early-stage tongue cancer, and may serve as a valuable decision-making tool to determine the appropriate treatment and avoid unnecessary neck surgery in patients without OLNM.

Imaging Resonance Effects in C + H2 Collisions Using a Zeeman Decelerator.

An intriguing phenomenon in molecular collisions is the occurrence of scattering resonances, which originate from bound and quasi-bound states supported by the interaction potential at low collision energies. The resonance effects in the scattering behavior are extraordinarily sensitive to the interaction potential, and their observation provides one of the most stringent tests for theoretical models. We present high-resolution measurements of state-resolved angular scattering distributions for inelastic collisions between Zeeman-decelerated C(3P1) atoms and para-H2 molecules at collision energies ranging from 77 cm-1 down to 0.5 cm-1. Rapid variations in the angular distributions were observed, which can be attributed to the consecutive reduction of contributing partial waves and effects of scattering resonances. The measurements showed excellent agreement with distributions predicted by ab initio quantum scattering calculations. However, discrepancies were found at specific collision energies, which most likely originate from an incorrectly predicted quasi-bound state. These observations provide exciting prospects for further high-precision and low-energy investigations of scattering processes that involve paramagnetic species.

Active Dendrite Suppression by Ferroelectric Membrane Separators in Rechargeable Batteries.

Anodic dendrite formation is a critical issue in rechargeable batteries and often leads to poor cycling stability and quick capacity loss. Prevailing strategies for dendrite suppression aim at slowing down the growth rate kinetically but still leaving possibilities for dendrite evolution over time. Herein, we report a complete dendrite elimination strategy using a mesoporous ferroelectric polymer membrane as the battery separator. The dendrite suppression is realized by spontaneously reversing the surface energetics for metal ion reduction at the protrusion front, where a positive piezoelectric polarization is generated and superimposed as the protrusion compresses the separator. This effect is demonstrated first in a Zn electroplating process, and further in Zn-Zn symmetric cells and Zn-NaV3O8·1.5H2O full cells, where the dendritic Zn anode surfaces are completely turned into featureless flat surfaces. Consequently, a substantially longer charging/discharging cycle is achieved. This study provides a promising pathway toward high-performance dendrite-free rechargeable batteries.