Anomalous Hall effect and electronic correlation in a spin-reoriented kagome antiferromagnet LuFe_(6)Sn_(6)

The kagome lattice system has been identified as a fertile ground for the emergence of a number of new quantumstates,including superconductivity,quantum spin liquids,and topological electronic states.This has attracted significantinterest within the field of condensed matter physics.Here,we present the observation of an anomalous Hall effect in aniron-based kagome antiferromagnet LuFe_(6)Sn_(6),which implies a non-zero Berry curvature in this compound.By means ofextensive magnetic measurements,a high Neel temperature,T_(N)=552 K,and a spin reorientation behavior were identifiedand a simple temperature-field phase diagram was constructed.Furthermore,this compound was found to exhibit a largeSommerfeld coefficient ofγ=87 mJ·mol^(-1)·K^(-2),suggesting the presence of a strong electronic correlation effect.Ourresearch indicates that LuFe_(6)Sn_(6)is an intriguing compound that may exhibit magnetism,strong correlation,and topologicalstates.

CeAu_(2)In_(4):A candidate of quasi-one-dimensional antiferromagnetic Kondo lattice

Needle-like single crystals of CeAu_(2)In_(4)have been grown from In flux and characterized as a new candidate of quasi-one-dimensional Kondo lattice compound by crystallographic,magnetic,transport,and specific-heat measurements down to very low temperatures.We observe an antiferromagnetic transition at T_(N)≈0.9 K,a highly non-mean-field profile of the corresponding peak in specific heat,and a large Sommerfeld coefficientγ=369 mJ·mol^(-1)·K^(-2).The Kondo temperature T_(K)is estimated to be 1.1 K,being low and comparable to TN.While Fermi liquid behavior is observed deep into the magnetically ordered phase,the Kadowaki-Woods ratio is much reduced relative to the expected value for Ce compounds with Kramers doublet ground state.Markedly,this feature shares striking similarities to that of the prototypical quasi-one-dimensional compounds YbNi_(4)P_(2) and CeRh_(6)Ge_(4) with tunable ferromagnetic quantum critical point.Given the shortest Ce-Ce distance along the needle direction,CeAu_(2)In_(4)appears to be an interesting model system for exploring antiferromagnetic quantum critical behaviors in a quasi-one-dimensional Kondo lattice with enhanced quantum fluctuations.

Quantum phase transitions in CePdAl probed by ultrasonic and thermoelectric measurements

CePdAl has been recently recognized as a frustrated antiferromagnetic heavy-fermion compound with a pressureor field-tuned,extended quantum critical phase at zero temperature.Identifying characteristic signatures of the emerging quantum critical phase,which are expected to be distinct from those near a quantum critical point,remains challenging.In this work,by performing ultrasonic and thermoelectric measurements down to very low temperatures in a^(3)He–^(4)He dilution refrigerator in the presence of magnetic field,we are able to obtain some crucial thermodynamic and thermal transport features of the quantum critical phase,including a frustration-related elastic softening detected by ultrasound and a Fermi-surface change probed by thermoelectric effect.

Unusual thermodynamics of low-energy phonons in the Dirac semimetal Cd_(3)As_(2)

By studying the thermal conductivity,specific heat,elastic modulus,and thermal expansion as a function of temperature for Cd_(3)As_(2),we have unveiled a couple of important thermodynamic features of the low-energy phonons strongly interacting with Dirac electrons.The existence of soft optical phonons,as inferred from the extremely low thermal conductivity,is unambiguously confirmed by low-temperature specific heat revealing significant deviation from Debye's description.The estimated Debye temperature is small in the range of 100-200 K and varies significantly depending upon the measurement used in its experimental determination.The thermodynamic Gr¨uneisen ratioγreveals a remarkable reduction below about 100 K,an energy scale that is highly relevant to the Dirac states,towards negative values below about 10 K that are indicative of lattice instability.

Structural Determination,Unstable Antiferromagnetism and Transport Properties of Fe-Kagome Y_(0.5)Fe_(3)Sn_(3) Single Crystals

Kagome materials have been studied intensively in condensed matter physics.With rich properties,various Kagome materials emerge during this process.Here,we grew single crystals of Y_(0.5)Fe_(3)Sn_(3)and confirmed an YCo_(6)Ge_(6)-type Kagome-lattice structure by detailed crystal structure characterizations.This compound bears an antiferromagnetic ordering at T_(N)= 551 K,and shows a weak ferromagnetism at low temperatures,where an anomalous Hall effect was observed,suggesting the non-zero Berry curvature.With the unstable antiferromagnetic ground state,our systematic investigations make Y_(0.5)Fe_(3)Sn_(3)a potential Kagome compound for Kagome or topological physics.

Spatial-Spectral Joint Network for Cholangiocarcinoma Microscopic Hyperspectral Image Classification

Accurate histopathology classification is a crucial factor in the diagnosis and treatment of Cholangiocarcinoma(CCA).Hyperspectral images(HSI)provide rich spectral information than ordinary RGB images,making them more useful for medical diagnosis.The Convolutional Neural Network(CNN)is commonly employed in hyperspectral image classification due to its remarkable capacity for feature extraction and image classification.However,many existing CNN-based HSI classification methods tend to ignore the importance of image spatial context information and the interdependence between spectral channels,leading to unsatisfied classification performance.Thus,to address these issues,this paper proposes a Spatial-Spectral Joint Network(SSJN)model for hyperspectral image classification that utilizes spatial self-attention and spectral feature extraction.The SSJN model is derived from the ResNet18 network and implemented with the non-local and Coordinate Attention(CA)modules,which extract long-range dependencies on image space and enhance spatial features through the Branch Attention(BA)module to emphasize the region of interest.Furthermore,the SSJN model employs Conv-LSTM modules to extract long-range depen-dencies in the image spectral domain.This addresses the gradient disappearance/explosion phenom-ena and enhances the model classification accuracy.The experimental results show that the pro-posed SSJN model is more efficient in leveraging the spatial and spectral information of hyperspec-tral images on multidimensional microspectral datasets of CCA,leading to higher classification accuracy,and may have useful references for medical diagnosis of CCA.

Near-infrared laser-activated aggregation-induced emission nanoparticles boost tumor carbonyl stress and immunotherapy of breast cancer

The induction of tumor carbonyl stress is reported to efficiently revert immune suppression in the tumor microenvironment and enhance cancer immunotherapy.However,low oxygen concentration due to inherent tumor hypoxia limits its catalytic effect.Herein,an injectable thermosensitive hydrogel system(named APH)is developed for co-loading of near-infrared(NIR)aggregation-induced emission(AIE)nanoparticles and plasma amine oxidase(PAO)for boosting carbonyl stress and enhancing antitumor immunity.Upon 808 nm NIR laser irradiation,the AIE nanoparticles trigger a mild-temperature(around 45◦C)photothermal effect in the tumor site,which significantly relieves tumor hypoxia and promotes the catalytic effect of released PAO to inhibit the growth of Myeloid-derived suppressor cells.Remarkably,the synergistic therapeutic effect of APH is verified through a significant inhibitory effect on the distant tumor,enhanced immune memory,and effective suppression of postoperative recurrence,rechallenge,and metastasis.Overall,the combined effect of AIE-mediated photothermal therapy and carbonyl stress by APH upon NIR irradiation therapy can significantly activate cancer immunotherapy,making it a promising treatment approach for cancer treatment.

The Impact of New Regulations on Prevention and Control of E-Cigarettes on Adolescents in Middle Schools—A City in China,2022–2023

What is already known about this topic?To protect the health of young people from the harmful impacts of electronic cigarettes(e-cigarettes),China has enacted various policies and regulations since 2018.As of October 1,2022,the Electronic Cigarette Management Measures were put into action.They prohibited the sale of flavored e-cigarettes,permitting only those of plain tobacco flavor to be sold.What is added by this report?The illegal market for flavored e-cigarettes,often disguised as milk tea cups,cola cans,and violent bear images,continues to flourish.There is an increased need to bolster support for the prohibition of flavored e-cigarettes and enhance public awareness of associated regulations.What are the implications for public health practice?To advance the health of China’s youth,it is crucial to improve the implementation and understanding of ecigarette policies and guidelines.

Phase imaging with an untrained neural network

Most of the neural networks proposed so far for computational imaging(CI)in optics employ a supervised training strategy,and thus need a large training set to optimize their weights and biases.Setting aside the requirements of environmental and system stability during many hours of data acquisition,in many practical applications,it is unlikely to be possible to obtain sufficient numbers of ground-truth images for training.Here,we propose to overcome this limitation by incorporating into a conventional deep neural network a complete physical model that represents the process of image formation.The most significant advantage of the resulting physics-enhanced deep neural network(PhysenNet)is that it can be used without training beforehand,thus eliminating the need for tens of thousands of labeled data.We take single-beam phase imaging as an example for demonstration.We experimentally show that one needs only to feed PhysenNet a single diffraction pattern of a phase object,and it can automatically optimize the network and eventually produce the object phase through the interplay between the neural network and the physical model.This opens up a new paradigm of neural network design,in which the concept of incorporating a physical model into a neural network can be generalized to solve many other CI problems.

Learning-based lensless imaging through optically thick scattering media

The problem of imaging through thick scattering media is encountered in many disciplines of science,ranging from mesoscopic physics to astronomy.Photons become diffusive after propagating through a scattering medium with an optical thickness of over 10 times the scattering mean free path.As a result,no image but only noise-like patterns can be directly formed.We propose a hybrid neural network for computational imaging through such thick scattering media,demonstrating the reconstruction of image information from various targets hidden behind a white polystyrene slab of 3 mm in thickness or 13.4 times the scattering mean free path.We also demonstrate that the target image can be retrieved with acceptable quality from a very small fraction of its scattered pattern,suggesting that the speckle pattern produced in this way is highly redundant.This leads to a profound question of how the information of the target being encoded into the speckle is to be addressed in future studies.

Biomimetic copper single-atom nanozyme system for self-enhanced nanocatalytic tumor therapy

Single-atom nanozymes(SAZs)with peroxidase(POD)-like activity have good nanocatalytic tumor therapy(NCT)capabilities.However,insufficient hydrogen peroxide(H2O2)and hydrogen ions in the cells limit their therapeutic effects.Herein,to overcome these limitations,a biomimetic single-atom nanozyme system was developed for self-enhanced NCT.We used a previously described approach to produce platelet membrane vesicles.Using a high-temperature carbonization approach,copper SAZs with excellent POD-like activity were successfully synthesized.Finally,through physical extrusion,a proton pump inhibitor(PPI;pantoprazole sodium)and the SAZs were combined with platelet membrane vesicles to create PPS.Both in vivo and in vitro,PPS displayed good tumor-targeting and accumulation abilities.PPIs were able to simultaneously regulate the hydrogen ion,glutathione(GSH),and H2O2 content in tumor cells,significantly improve the catalytic ability of SAZs,and achieve self-enhanced NCT.Our in vivo studies showed that PPS had a tumor suppression rate of>90%.PPS also limited the synthesis of GSH in cells at the source;thus,glutamine metabolism therapy and NCT were integrated into an innovative method,which provides a novel strategy for multimodal tumor therapy.

Review of Abnormality Detection and Fault Diagnosis Methods for Lithium‑Ion Batteries

Electric vehicles are developing prosperously in recent years.Lithium-ion batteries have become the dominant energy storage device in electric vehicle application because of its advantages such as high power density and long cycle life.To ensure safe and efficient battery operations and to enable timely battery system maintenance,accurate and reliable detection and diagnosis of battery faults are necessitated.In this paper,the state-of-the-art battery fault diagnosis methods are comprehensively reviewed.First,the degradation and fault mechanisms are analyzed and common abnormal behaviors are summarized.Then,the fault diagnosis methods are categorized into the statistical analysis-,model-,signal processing-,and data-driven methods.Their distinctive characteristics and applications are summarized and compared.Finally,the challenges facing the existing fault diagnosis methods are discussed and the future research directions are pointed out.

Multi‑scale Battery Modeling Method for Fault Diagnosis

Fault diagnosis is key to enhancing the performance and safety of battery storage systems.However,it is challenging to realize efficient fault diagnosis for lithium-ion batteries because the accuracy diagnostic algorithm is limited and the features of the different faults are similar.The model-based method has been widely used for degradation mechanism analysis,state estimation,and life prediction of lithium-ion battery systems due to the fast speed and high development efficiency.This paper reviews the mainstream modeling approaches used for battery diagnosis.First,a review of the battery’s degradation mechanisms and the external factors affecting the aging rate is presented.Second,the different modeling approaches are summarized,from microscopic to macroscopic scales,including density functional theory,molecular dynamics,X-ray computed tomography technology,electrochemical model,equivalent circuit model,distributed model and neural network algorithm.Subsequently,the advantages and disadvantages of these model approaches are discussed for fault detection and diagnosis of batteries in different application scenarios.Finally,the remaining challenges of model-based battery diagnosis and the future perspective of using cloud control and battery intelligent networking to enhance diagnostic performance are discussed.

Generic Seebeck effect from spin entropy

How magnetism affects the Seebeck effect is an important issue of wide concern in the thermoelectric community but remains elusive.Based on a thermodynamic analysis of spin degrees of freedom on varied d-electron-based ferromagnets and antiferromagnets,we demonstrate that in itinerant or partially itinerant magnetic compounds there exists a generic spin contribution to the Seebeck effect over an extended temperature range from slightly below to well above the magnetic transition temperature.This contribution is interpreted as resulting from transport spin entropy of(partially)delocalized conducting d electrons with strong thermal spin fluctuations,even semiquantitatively in a single-band case,in addition to the conventional diffusion part arising from their kinetic degrees of freedom.As a highly generic effect,the spin-dependent Seebeck effect might pave a feasible way toward efficient“magnetic thermoelectrics.”

Large transverse thermoelectric figure of merit in a topological Dirac semimetal

The Seebeck effect encounters a few fundamental constraints hindering its thermoelectric(TE)conversion efficiency.Most notably,there are the charge compensation of electrons and holes that diminishes this effect,and the Wiedemann-Franz(WF)law that makes independent optimization of the corresponding electrical and thermal conductivities impossible.Here,we demonstrate that in the topological Dirac semimetal Cd3As2 the Nernst effect,i.e.,the transverse counterpart of the Seebeck effect,can generate a large TE figure of merit zNT.At room temperature,zNT≈0.5 in a small field of 2 T and it significantly surmounts its longitudinal counterpart for any field.A large Nernst effect is generically expected in topological semimetals,benefiting from both the bipolar transport of compensated electrons and holes and their high mobilities.In this case,heat and charge transport are orthogonal,i.e.,not intertwined by the WF law anymore.More importantly,further optimization of zNT by tuning the Fermi level to the Dirac node can be anticipated due to not only the enhanced bipolar transport,but also the anomalous Nernst effect arising from a pronounced Berry curvature.A combination of the topologically trivial and nontrivial advantages promises to open a new avenue towards high-efficient transverse thermoelectricity.

Kagome晶格补偿型半金属Ni_(3)In_(2)S_(2)创纪录的高迁移率和极大磁电阻现象

具有Kagome晶格的晶体有很多有趣的性质,包括受挫磁阻、电荷有序、拓扑态、超导和关联现象.为了在电子学和自旋电子学应用中实现高性能Kagome晶格化合物,需要对能带结构仔细调整.本文采用输运测量、角分辨光电子能谱和从头计算等方法研究了Kagome晶格晶体Ni_(3)In_(2)S_(2)的电子结构.输运测量表明,Ni_(3)In_(2)S_(2)是一种在Kagome晶格材料中具有创纪录的高载流子迁移率(空穴和电子迁移率分别约为8683和7356 cm^(2)V^(-1)S^(-1))和极大磁电阻(在2 K和13 T时为15,518%)的补偿半金属.Ni在Kagome晶格中的3d电子导致的非直接带隙、小的电子/空穴口袋和大的带宽的能带结构特征很好地解释了这些特殊的性质.这项工作表明,晶体场和掺杂是优化Kagome晶格晶体输运特性的关键因素.我们的工作为Kagome晶格半金属在电子学和自旋电子学方面的应用提供了材料基础和优化路径.