Unlocking Li superionic conductivity in face-centred cubic oxides via face-sharing configurations.

Oxides with a face-centred cubic (fcc) anion sublattice are generally not considered as solid-state electrolytes as the structural framework is thought to be unfavourable for lithium (Li) superionic conduction. Here we demonstrate Li superionic conductivity in fcc-type oxides in which face-sharing Li configurations have been created through cation over-stoichiometry in rocksalt-type lattices via excess Li. We find that the face-sharing Li configurations create a novel spinel with unconventional stoichiometry and raise the energy of Li, thereby promoting fast Li-ion conduction. The over-stoichiometric Li-In-Sn-O compound exhibits a total Li superionic conductivity of 3.38 × 10-4 S cm-1 at room temperature with a low migration barrier of 255 meV. Our work unlocks the potential of designing Li superionic conductors in a prototypical structural framework with vast chemical flexibility, providing fertile ground for discovering new solid-state electrolytes.

Real-space charge-density imaging with sub-ångström resolution by four-dimensional electron microscopy.

The distribution of charge density in materials dictates their chemical bonding, electronic transport, and optical and mechanical properties. Indirectly measuring the charge density of bulk materials is possible through X-ray or electron diffraction techniques by fitting their structure factors1-3, but only if the sample is perfectly homogeneous within the area illuminated by the beam. Meanwhile, scanning tunnelling microscopy and atomic force microscopy enable us to see chemical bonds, but only on the surface4-6. It remains a challenge to resolve charge density in nanostructures and functional materials with imperfect crystalline structures-such as those with defects, interfaces or boundaries at which new physics emerges. Here we describe the development of a real-space imaging technique that can directly map the local charge density of crystalline materials with sub-ångström resolution, using scanning transmission electron microscopy alongside an angle-resolved pixellated fast-electron detector. Using this technique, we image the interfacial charge distribution and ferroelectric polarization in a SrTiO3/BiFeO3 heterojunction in four dimensions, and discover charge accumulation at the interface that is induced by the penetration of the polarization field of BiFeO3. We validate this finding through side-by-side comparison with density functional theory calculations. Our charge-density imaging method advances electron microscopy from detecting atoms to imaging electron distributions, providing a new way of studying local bonding in crystalline solids.

Analysis and experimental assessment of an optimized SERS substrate used to detect thiabendazole in apples with high sensitivity.

Pesticides that linger in the environment and ecosystems for an extended period can cause severe and dangerous health problems in humans. To detect pesticides in foods, the development of high-sensitivity and quick screening technologies was required. This research investigated the performance of Au@Ag NPs with varying thicknesses of the silver shell for detecting trace quantities of thiabendazole (TBZ) in apples using surface-enhanced Raman spectroscopy (SERS). The Au@Ag NPs were synthesized by coating 32 nm gold seeds with different thicknesses of silver shell ranging from 2.4 to 8.7 nm, achieved by adjusting the incorporation of AgNO3 and ascorbic acid. The optimized Au@Ag NPs with a 7.3 nm silver shell demonstrated outstanding SERS activity, high sensitivity, and a detection limit of 0.05 µg/mL for TBZ. The R2 values, representing the goodness of fit, were found to be 0.990 and 0.986 for standard and real TBZ samples, respectively, indicating a strong correlation between the measured signal and the TBZ concentration. The recovery analysis showed a reliable and accurate detection capability (96 to 105%), suggesting good reliability and accuracy of the SERS-based detection using the optimal Au@Ag NPs. Overall, this research highlights the potential of SERS with optimal Au@Ag NPs for rapid and effective monitoring of pesticides in the food industry.

Dislocation-Assisted Quasi-Two-Dimensional Semiconducting Nanochannels Embedded in Perovskite Thin Films.

Defect engineering in perovskite thin films has attracted extensive attention recently due to the films' atomic-scale modification, allowing for remarkable flexibility to design novel nanostructures for next generation nanodevices. However, the defect-assisted three-dimensional nanostructures in thin film matrices usually has large misfit strain and thus causes unstable thin film structures. In contrast, defect-assisted one- or two-dimensional nanostructures embedded in thin films can sustain large misfit strains without relaxation, which make them suitable for defect engineering in perovskite thin films. Here, we reported the fabrication and characterization of edge-type misfit dislocation-assisted two-dimensional BiMnOx nanochannels embedded in SrTiO3/La0.7Sr0.3MnO3/TbScO3 perovskite thin films. The nanochannels are epitaxially grown from the surrounding films without noticeable misfit strain. Diode-like current rectification was spatially observed at nanochannels due to the formation of Schottky junctions between BiMnOx nanochannels and conducting La0.7Sr0.3MnO3 thin films. Such atomically scaled heterostructures constitute more flexible ultimate functional units for nanoscale electronic devices.

High-speed Ge-on-GaAs photodetector.

In this work, a germanium (Ge) on gallium arsenide (GaAs) photodetector is demonstrated with the optical response from 850 nm to 1600 nm, which has potential for monolithic integration with VCSELs on GaAs platform as transceiver working beyond 900 nm. The device exhibits a responsivity of 0.35A/W, 0.39 A/W and 0.11 A/W at 1000 nm, 1310 nm and 1550 nm, respectively and dark current of 8 nA at -1 V. The 10 µm diameter back-illuminated device achieves a 3-dB bandwidth of 9.3 GHz under -2 V bias. A donor-like trap at the interface between the Ge and GaAs collection layers is verified by capacitance-voltage curve and deep-level transient spectroscopy (DLTS) measurement, which impedes the depletion in GaAs collection layers.

Ultrasensitive detection of plant hormone abscisic acid-based surface-enhanced Raman spectroscopy aptamer sensor.

Abscisic acid (ABA), as the most common plant hormone in the growth of wheat, can greatly affect the yield when its levels deviate from normal. Therefore, highly sensitive and selective detection of this hormone is greatly needed. In this work, we developed an aptamer sensor based on surface-enhanced Raman spectroscopy (SERS) and applied it for the high sensitivity detection of ABA. Biotin-modified ABA aptamer complement chains were modified on ferrosoferric oxide magnetic nanoparticles (Fe3O4MNPs) and acted as capture probes, and sulfhydryl aptamer (SH-Apt)-modified silver-coated gold nanospheres (Au@Ag NPs) were used as signal probes. Through the recognition of the ABA aptamer and its complementary chains, an aptamer sensor based on SERS was constructed. As SERS internal standard molecules of 4-mercaptobenzoic acid (4-MBA) were encapsulated between the gold core and silver shell of the signal probes; the constructed aptamer sensor generated a strong SERS signal of 4-MBA after magnetic separation. When there were ABA molecules in the detection system, with the preferential binding of ABA aptamer and ABA molecule, the signal probes were released from the capture probes, after magnetic separation, leading to a linear decrease in SERS intensity of 4-MBA. Thus, the detection response was linear over a logarithmic concentration range, with an ultra-low detection limit of 0.67 fM. In addition, the practical use of this assay method was demonstrated in ABA detection from fresh wheat leaves, with a relative error (RE) of 5.43-8.94% when compared with results from enzyme-linked immunosorbent assay (ELISA). The low RE value proves that the aptamer sensor will be a promising method for ABA detection.

Satellite-based phenology products and in-situ pollen dynamics: A comparative assessment.

Ongoing climate variability and change is impacting pollen exposure dynamics among sensitive populations. However, pollen data that can provide beneficial information to allergy experts and patients alike remains elusive. The lack of high spatial resolution pollen data has resulted in a growing interest in using phenology information that is derived using satellite observations to infer key pollen events including start of pollen season (SPS), timing of peak pollen season (PPS), and length of pollen season (LPS). However, it remains unclear if the agreement between satellite-based phenology information (e.g. start of season: SOS) and the in-situ pollen dynamics vary based on the type of satellite product itself or the processing methods used. To address this, we investigated the relationship between vegetation phenology indicator (SOS) derived from two separate sensor/satellite observations (MODIS, Landsat), and two different processing methods (double logistic regression (DLM) vs hybrid piecewise logistic regression (HPLM)) with in-situ pollen season dynamics (SPS, PPS, LPS) for three dominant allergenic tree pollen species (birch, oak, and poplar) that dominate the springtime allergy season in North America. Our results showed that irrespective of the data processing method (i.e. DLM vs HPLM), the MODIS-based SOS to be more closely aligned with the in-situ SPS, and PPS while upscaled Landsat based SOS had a better precision. The data products obtained using DLM processing methods tended to perform better than the HPLM based methods. We further showed that MODIS based phenology information along with temperature and latitude can be used to infer in-situ pollen dynamic for tree pollen during spring time. Our findings suggest that satellite-based phenology information may be useful in the development of early warning systems for allergic diseases.

Hidden Subsurface Reconstruction and Its Atomic Origins in Layered Oxide Cathodes.

Structural transformations near surfaces of solid-state materials underpin functional mechanisms of a broad range of applications including catalysis, memory, and energy storage. It has been a long-standing notion that the outermost free surfaces, accompanied by broken translational symmetry and altered atomic configurations, are usually the birthplace for structural transformations. Here, in a layered oxide cathode for Li-ion batteries, we for the first time observe the incipient state of the well-documented layered-to-spinel-like structural transformation, which is surprisingly initiated from the subsurface layer, rather than the very surface. Coupling atomic level scanning transmission electron microscopy imaging with electron energy loss spectroscopy, we discover that the reconstructed subsurfaces, featuring a mix of discrete patches of layered and spinel-like structures, are associated with selective atomic species partition and consequent nanoscale nonuniform composition gradient distribution at the subsurface. Our findings provide fundamental insights on atomic-scale mechanisms of structural transformation in layered cathodes.

Observation of Strong Polarization Enhancement in Ferroelectric Tunnel Junctions.

Ferroelectric heterostructures, with capability of storing data at ultrahigh densities, could act as the platform for next-generation memories. The development of new device paradigms has been hampered by the long-standing notion of inevitable ferroelectricity suppression under reduced dimensions. Despite recent experimental observation of stable polarized states in ferroelectric ultrathin films, the out-of-plane polarization components in these films are strongly attenuated compared to thicker films, implying a degradation of device performance in electronic miniaturization processes. Here, in a model system of BiFeO3/La0.7Sr0.3MnO3, we report observation of a dramatic out-of-plane polarization enhancement that occurs with decreasing film thickness. Our electron microscopy analysis coupled with phase-field simulations reveals a polarization-enhancement mechanism that is dominated by the accumulation of oxygen vacancies at interfacial layers. The results shed light on the interplay between polarization and defects in nanoscale ferroelectrics and suggest a route to enhance functionality in oxide devices.

Real-time studies of ferroelectric domain switching: a review.

Ferroelectric materials have been utilized in a broad range of electronic, optical, and electromechanical applications and hold the promise for the design of future high-density nonvolatile memories and multifunctional nano-devices. The applications of ferroelectric materials stem from the ability to switch polarized domains by applying an electric field, and therefore a fundamental understanding of the switching dynamics is critical for design of practical devices. In this review, we summarize the progress in the study of the microscopic process of ferroelectric domain switching using recently developed in situ transmission electron microscopy (TEM). We first briefly introduce the instrumentation, experimental procedures, imaging mechanisms, and analytical methods of the state-of-the-art in situ TEM techniques. The application of these techniques to studying a wide range of complex switching phenomena, including domain nucleation, domain wall motion, domain relaxation, domain-defect interaction, and the interplay between different types of domains, is demonstrated. The underlying physics of these dynamic processes are discussed.

Intercorrelated In-Plane and Out-of-Plane Ferroelectricity in Ultrathin Two-Dimensional Layered Semiconductor In2Se3.

Enriching the functionality of ferroelectric materials with visible-light sensitivity and multiaxial switching capability would open up new opportunities for their applications in advanced information storage with diverse signal manipulation functions. We report experimental observations of robust intralayer ferroelectricity in two-dimensional (2D) van der Waals layered α-In2Se3 ultrathin flakes at room temperature. Distinct from other 2D and conventional ferroelectrics, In2Se3 exhibits intrinsically intercorrelated out-of-plane and in-plane polarization, where the reversal of the out-of-plane polarization by a vertical electric field also induces the rotation of the in-plane polarization. On the basis of the in-plane switchable diode effect and the narrow bandgap (∼1.3 eV) of ferroelectric In2Se3, a prototypical nonvolatile memory device, which can be manipulated both by electric field and visible light illumination, is demonstrated for advancing data storage technologies.

Defect-Induced Hedgehog Polarization States in Multiferroics.

Continuous developments in nanotechnology require new approaches to materials synthesis that can produce novel functional structures. Here, we show that nanoscale defects, such as nonstoichiometric nanoregions (NSNRs), can act as nano-building blocks for creating complex electrical polarization structures in the prototypical multiferroic BiFeO_{3}. An array of charged NSNRs are produced in BiFeO_{3} thin films by tuning the substrate temperature during film growth. Atomic-scale scanning transmission electron microscopy imaging reveals exotic polarization rotation patterns around these NSNRs. These polarization patterns resemble hedgehog or vortex topologies and can cause local changes in lattice symmetries leading to mixed-phase structures resembling the morphotropic phase boundary with high piezoelectricity. Phase-field simulations indicate that the observed polarization configurations are mainly induced by charged states at the NSNRs. Engineering defects thus may provide a new route for developing ferroelectric- or multiferroic-based nanodevices.

Methods of Population Spatialization Based on the Classification Information of Buildings from China's First National Geoinformation Survey in Urban Area: A Case Study of Wuchang District, Wuhan City, China.

Most of the currently mature methods that are used globally for population spatialization are researched on a single level, and are dependent on the spatial relationship between population and land covers (city, road, water area, etc.), resulting in difficulties in data acquisition and an inability to identify precise features on the different levels. This paper proposes a multi-level population spatialization method on the different administrative levels with the support of China's first national geoinformation survey, and then considers several approaches to verify the results of the multi-level method. This paper aims to establish a multi-level population spatialization method that is suitable for the administrative division of districts and streets. It is assumed that the same residential house has the same population density on the district level. Based on this assumption, the least squares regression model is used to obtain the optimized prediction model and accurate population space prediction results by dynamically segmenting and aggregating house categories.In addition, it is assumed that the distribution of the population is relatively regular in communities that are spatially close to each other, and that the population densities on the street level are similar, so the average population density is assessed by optimizing the community and surrounding residential houses on the street level. Finally, the scientificalness and rationality of the proposed method is proved by spatial autocorrelation analysis, overlay analysis, cross-validation analysis and accuracy assessment methods.

Atomic-Scale Mechanisms of Defect-Induced Retention Failure in Ferroelectrics.

The ability to switch the ferroelectric polarization using an electric field makes ferroelectrics attractive for application in nanodevices such as high-density memories. One of the major challenges impeding this application, however, has been known as "retention failure", which is a spontaneous process of polarization back-switching that can lead to data loss. This process is generally thought to be caused by the domain instability arising from interface boundary conditions and countered by defects, which can pin the domain wall and impede the back-switching. Here, using in situ transmission electron microscopy and atomic-scale scanning transmission electron microscopy, we show that the polarization retention failure can be induced by commonly observed nanoscale impurity defects in BiFeO3 thin films. The interaction between polarization and the defects can also lead to the stabilization of novel functional nanodomains with mixed-phase structures and head-to-head polarization configurations. Thus, defect engineering provides a new route for tuning properties of ferroelectric nanosystems.

Atomic scale structure changes induced by charged domain walls in ferroelectric materials.

Charged domain walls (CDWs) are of significant scientific and technological importance as they have been shown to play a critical role in controlling the switching mechanism and electric, photoelectric, and piezoelectric properties of ferroelectric materials. The atomic scale structure and properties of CDWs, which are critical for understanding the emergent properties, have, however, been rarely explored. In this work, using a spherical-aberration-corrected transmission electron microscope with subangstrom resolution, we have found that the polarization bound charge of the CDW in rhombohedral-like BiFeO3 thin films not only induces the formation of a tetragonal-like crystal structure at the CDW but also stabilizes unexpected nanosized domains with new polarization states and unconventional domain walls. These findings provide new insights on the effects of bound charge on ferroelectric domain structures and are critical for understanding the electrical switching in ferroelectric thin films as well as in memory devices.

A Comparative Study on the Effect of Substrate Structure on Electrochemical Performance and Stability of Electrodeposited Platinum and Iridium Oxide Coatings for Neural Electrodes.

Implantable electrodes are crucial for stimulation safety and recording quality of neuronal activity. To enhance their electrochemical performance, electrodeposited nanostructured platinum (nanoPt) and iridium oxide (IrOx) have been proposed due to their advantages of in situ deposition and ease of processing. However, their unstable adhesion has been a challenge in practical applications. This study investigated the electrochemical performance and stability of nanoPt and IrOx coatings on hierarchical platinum-iridium (Pt-Ir) substrates prepared by femtosecond laser, compared with the coatings on smooth Pt-Ir substrates. Ultrasonic testing, agarose gel testing, and cyclic voltammetry (CV) testing were used to evaluate the coatings' stability. Results showed that the hierarchical Pt-Ir substrate significantly enhanced the charge-storage capacity of electrodes with both coatings to more than 330 mC/cm2, which was over 75 times that of the smooth Pt-Ir electrode. The hierarchical substrate could also reduce the cracking of nanoPt coatings after ultrasonic, agarose gel and CV testing. Although some shedding was observed in the IrOx coating on the hierarchical substrate after one hour of sonication, it showed good stability in the agarose gel and CV tests. Stable nanoPt and IrOx coatings may not only improve the electrochemical performance but also benefit the function of neurobiochemical detection.

Early-stage oral cancer diagnosis by artificial intelligence-based SERS using Ag NWs@ZIF core-shell nanochains.

Surface-enhanced Raman spectroscopy (SERS) has great potential in the early diagnosis of diseases by detecting the changes of volatile biomarkers in exhaled breath, because of its high sensitivity, rich chemical molecular fingerprint information, and immunity to humidity. Here, an accurate diagnosis of oral cancer (OC) is demonstrated using artificial intelligence (AI)-based SERS of exhaled breath in plasmonic-metal organic framework (MOF) nanoparticles. These plasmonic-MOF nanoparticles were prepared using a zeolitic imidazolate framework coated on Ag nanowires (Ag NWs@ZIF), which offers Raman enhancement from the plasmonic nanowires and gas enrichment from the ZIF shells. Then, the core-shell nanochains of Ag NWs@ZIF prepared with 0.5 mL Ag NWs were selected to capture gaseous methanethiol, which is a tumor biomarker, from the exhalation of OC patients. The substrate was used to collect a total of 400 SERS spectra of exhaled breath of simulated healthy people and simulated OC patients. The artificial neural network (ANN) model in the AI algorithm was trained with these SERS spectra and could classify them with an accuracy of 99%. Notably, the model predicted OC with an area under the curve (AUC) of 0.996 for the simulated OC breath samples. This work suggests the great potential of the combination of breath analysis and AI as a method for the early-stage diagnosis of oral cancer.

Atomic-scale probing of short-range order and its impact on electrochemical properties in cation-disordered oxide cathodes.

Chemical short-range-order has been widely noticed to dictate the electrochemical properties of Li-excess cation-disordered rocksalt oxides, a class of cathode based on earth abundant elements for next-generation high-energy-density batteries. Existence of short-range-order is normally evidenced by a diffused intensity pattern in reciprocal space, however, derivation of local atomic arrangements of short-range-order in real space is hardly possible. Here, by a combination of aberration-corrected scanning transmission electron microscopy, electron diffraction, and cluster-expansion Monte Carlo simulations, we reveal the short-range-order is a convolution of three basic types: tetrahedron, octahedron, and cube. We discover that short-range-order directly correlates with Li percolation channels, which correspondingly affects Li transport behavior. We further demonstrate that short-range-order can be effectively manipulated by anion doping or post-synthesis thermal treatment, creating new avenues for tailoring the electrochemical properties. Our results provide fundamental insights for decoding the complex relationship between local chemical ordering and properties of crystalline compounds.

AI and machine learning for soil analysis: an assessment of sustainable agricultural practices.

Sustainable agricultural practices help to manage and use natural resources efficiently. Due to global climate and geospatial land design, soil texture, soil-water content (SWC), and other parameters vary greatly; thus, real time, robust, and accurate soil analytical measurements are difficult to be developed. Conventional statistical analysis tools take longer to analyze and interpret data, which may have delayed a crucial decision. Therefore, this review paper is presented to develop the researcher's insight toward robust, accurate, and quick soil analysis using artificial intelligence (AI), deep learning (DL), and machine learning (ML) platforms to attain robustness in SWC and soil texture analysis. Machine learning algorithms, such as random forests, support vector machines, and neural networks, can be employed to develop predictive models based on available soil data and auxiliary environmental variables. Geostatistical techniques, including kriging and co-kriging, help interpolate and extrapolate soil property values to unsampled locations, improving the spatial representation of the data set. The false positivity in SWC results and bugs in advanced detection techniques are also evaluated, which may lead to wrong agricultural practices. Moreover, the advantages of AI data processing over general statistical analysis for robust and noise-free results have also been discussed in light of smart irrigation technologies. Conclusively, the conventional statistical tools for SWCs and soil texture analysis are not enough to practice and manage ergonomic land management. The broader geospatial non-numeric data are more suitable for AI processing that may soon help soil scientists develop a global SWC database.