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.

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.

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.

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.

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.

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.

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.

Research Progress of Steels for Nuclear Reactor Pressure Vessels.

The nuclear reactor pressure vessel is an important component of a nuclear power plant. It has been used in harsh environments such as high temperature, high pressure, neutron irradiation, thermal aging, corrosion and fatigue for a long time, which puts forward higher standards for the performance requirements for nuclear pressure vessel steel. Based on the characteristics of large size and wall thickness of the nuclear pressure vessel, combined with its performance requirements, this work studies the problems of forging technology, mechanical properties, irradiation damage, corrosion failure, thermal aging behavior and fatigue properties, and summarizes the research progress of nuclear pressure vessel materials. The influencing factors of microstructures evolution and mechanism of mechanical properties change of nuclear pressure vessel steel are analyzed in this work. The mechanical properties before and after irradiation are compared, and the influence mechanisms of irradiation hardening and embrittlement are also summarized. Although the stainless steel will be surfacing on the inner wall of nuclear pressure vessel to prevent corrosion, long-term operation may cause aging or deterioration of stainless steel, resulting in corrosion caused by the contact between the primary circuit water environment and the nuclear pressure vessel steel. Therefore, the corrosion behavior of nuclear pressure vessels materials is also summarized in detail. Meanwhile, the evolution mechanism of the microstructure of nuclear pressure vessel materials under thermal aging conditions is analyzed, and the mechanisms affecting the mechanical properties are also described. In addition, the influence mechanisms of internal and external factors on the fatigue properties, fatigue crack initiation and fatigue crack propagation of nuclear pressure vessel steel are analyzed in detail from different perspectives. Finally, the development direction and further research contents of nuclear pressure vessel materials are prospected in order to improve the service life and ensure safe service in harsh environment.

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.

Electrochemical and biological performance of hierarchical platinum-iridium electrodes structured by a femtosecond laser.

Neural electrode interfaces are essential to the stimulation safety and recording quality of various bioelectronic therapies. The recently proposed hierarchical platinum-iridium (Pt-Ir) electrodes produced by femtosecond lasers have exhibited superior electrochemical performance in vitro, but their in vivo performance is still unclear. In this study, we explored the electrochemical performance, biological response, and tissue adhesion of hierarchical Pt-Ir electrodes by implantation in adult rat brains for 1, 8, and 16 weeks. Regular smooth Pt-Ir electrodes were used as a control. The results showed that the electrochemical performance of both electrodes decreased and leveled off during implantation. However, after 16 weeks, the charge storage capacity of hierarchical electrodes stabilized at ~16.8 mC/cm2, which was 15 times that of the smooth control electrodes (1.1 mC/cm2). Moreover, the highly structured electrodes had lower impedance amplitude and cutoff frequency values. The similar histological response to smooth electrodes indicated good biocompatibility of the hierarchically structured Pt-Ir electrodes. Given their superior in vivo performance, the femtosecond laser-treated Pt-Ir electrode showed great potential for neuromodulation applications.

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.

Fluorination-Enhanced Surface Stability of Disordered Rocksalt Cathodes.

Cation-disordered rocksalt (DRX) oxides are a promising new class of high-energy-density cathode materials for next-generation Li-ion batteries. However, their capacity fade presents a major challenge. Partial fluorine (F) substitution into the oxygen (O) lattice appears to be an effective strategy for improving the cycling stability, but the underlying atomistic mechanism remains elusive. Here, using a combination of advanced transmission electron microscopy based imaging and spectroscopy techniques, the structural and chemical evolution upon cycling of Mn-based DRX cathodes with an increasing F content (Li-Mn-Nb-O-Fx , x = 0, 0.05, 0.2) are probed. The atomic origin behind the beneficial effect of high-level fluorination for enhancing the surface stability of the DRX is revealed. It is discovered that, due to the reduced O redox activity while with increasing F concentration, F in the DRX lattice mitigates the formation of an O-deficient surface layer upon cycling. For low F-substituted DRX, the O loss near the surface results in the formation of an amorphous cathode-electrolyte interphase layer and nanoscale voids after extended cycling. Increased F concentration in the DRX lattice minimizes both O loss and the interfacial reactions between DRX and the liquid electrolyte, enhancing the surface stability of DRX. These results provide guidance on the development of next-generation cathode materials through anion substitution.

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.

LT-LiMn0.5Ni0.5O2: a unique co-free cathode for high energy Li-ion cells.

A novel LiMn0.5Ni0.5O2 cathode with a predominant, partially-disordered lithiated-spinel structure has been prepared by a 'low temperature' (LT) synthesis. Li/LT-LiMn0.5Ni0.5O2 cells operate between 5.0 and 2.5 V with good cycling stability, yielding a capacity of 225 mA h g-1, principally by redox reactions on the nickel ions on distinct voltage plateaus at ∼3.6 V and ∼4.6 V.

Formation of LiF Surface Layer During Direct Fluorination of High-Capacity Co-Free Disordered Rocksalt Cathodes.

Disordered rocksalt (DRX) cathodes have attracted interest due to their high capacity and compositional flexibility (e.g., Co-free chemistries). However, the sloping voltage profile and gradual capacity fade during cycling have hindered the widespread adoption of these materials. Simulations predict that fluorine substitution in DRX cathodes will improve their capacity, rate performance, and cyclability. In this study, we use a fluidized bed reactor to fluorinate a model Li-rich DRX composition (Li1.15Ni0.375Ti0.375Mo0.1O2, NTMO) to investigate how fluorine content impacts the cathode's structure and electrochemical performance. Instead of substituting O with F to form oxyfluoride phases, direct fluorination of DRX cathodes leads to the formation of LiF surface films, which improves the specific energy and capacity retention. This study demonstrates the feasibility of direct fluorination to improve the electrochemical performance of high-voltage cathodes by tuning the material's surface chemistry.

Interplay between Cation and Anion Redox in Ni-Based Disordered Rocksalt Cathodes.

The reversibility of the redox processes plays a crucial role in the electrochemical performance of lithium-excess cation-disordered rocksalt (DRX) cathodes. Here, we report a comprehensive analysis of the redox reactions in a representative Ni-based DRX cathode. The aim of this work is to elucidate the roles of multiple cations and anions in the charge compensation mechanism that is ultimately linked to the electrochemical performance of Ni-based DRX cathode. The low-voltage reduction reaction results in the low energy efficiency and strong voltage hysteresis. Our data reveal that the Mo migration between octahedral and tetrahedral sites enhances the O reduction potential, thus offering a potential strategy to improve energy efficiency. This work highlights the important role that the high-valence transition metal plays in the redox chemistry and provides useful insights into the potential pathway to further address the challenges in Ni-based DRX systems.

Investigation of Artifacts and Optimization in Proton Resonance Frequency Thermometry Towards Heating Risk Monitoring of Implantable Medical Devices in Magnetic Resonance Imaging.

OBJECTIVE: Artifacts limit the application of proton resonance frequency (PRF) thermometry for on-site, individualized heating evaluations of implantable medical devices such as deep brain stimulation (DBS) for use in magnetic resonance imaging (MRI). Its properties are unclear and the research on how to choose an unaffected measurement region is insufficient. METHODS: The properties of PRF signals around the metallic DBS electrode were investigated through simulations and phantom experiments considering electromagnetic interferences from material susceptibility and the radio frequency (RF) interactions. A threshold method on phase difference Δϕ was used to define a measurement area to estimate heating at the electrode surface. Its performance was compared to that of the Bayesian magnitude method and probe measurements. RESULTS: The B0 magnetic field inhomogeneity due to the electrode susceptibility was the main influencing factor on PRF compared to the RF artifact. Δϕ around the electrode followed normal distribution but was distorted. Underestimation occurred at places with high temperature rises. The noise was increased and could be well estimated from magnitude images using a modified NEMA method. The Δϕ-threshold method based on this knowledge outperformed the Bayesian magnitude method by more than 42% in estimation error of the electrode heating. CONCLUSION: The findings favor the use of PRF with the proposed approach as a reliable method for electrode heating estimation. SIGNIFICANCE: This study clarified the influence of device artifacts and could improve the performance of PRF thermometry for individualized heating assessments of patients with implants under MRI.

Origin, Nature, and the Dynamic Behavior of Nanoscale Vacancy Clusters in Ni-Rich Layered Oxide Cathodes.

Effects of nanoscale vacancy clusters on the electrochemical properties of cathodes critically depend on the dynamic characteristics of vacancies during the battery cycling. However, a fundamental understanding of vacancy clusters in the layer-structured cathode remains elusive. Here, using scanning transmission electron microscopy, we reveal a cycling-induced vacancy aggregation behavior in a layer-structured cathode. We discover that during the initial charging, vacancies aggregate to form nanoclusters at the outer layer of the secondary particle, which subsequently extend to the inner part of the particle when fully charged. With extended cycling, these nanoscale vacancy clusters become immobilized. We further reveal that the generation of these vacancy clusters is correlated to the material synthesis conditions. Our findings solve a long-standing puzzle on the origin, nature, and behavior of the commonly visible vacancy clusters in the layered cathode, providing insights into correlation between properties and dynamic behaviors of atomic-scale defects in layered oxide cathodes.