Birefringent, low loss, and broadband semi-tube anti-resonant hollow-core fiber.

We report on the design, fabrication, and characterization of a low-loss birefringent semi-tube anti-resonant hollow-core fiber (AR-HCF). By optimizing the structure design and the stack-and-draw fabrication technique, a transmission loss of 4.8 dB/km at 1522 nm, a <10 dB/km bandwidth of 154 nm, and a phase birefringence of 1.8 × 10-5 are demonstrated. This achieved loss is more than one order of magnitude lower than the previously reported birefringent AR-HCF and the bandwidth is one order of magnitude broader than the reported birefringent photonic bandgap hollow-core fiber (PBG-HCF) with the same loss level. The polarization extinction ratio (PER) reaches the ∼20 dB level in a 90 m-long fiber under >25 cm bending radius. Combined with the single mode and low dispersion features, the developed semi-tube AR-HCF may find a variety of applications in frequency metrology, interferometric fiber gyroscopes, and long-baseline stellar interferometry.

Feature fusion improves performance and interpretability of machine learning models in identifying soil pollution of potentially contaminated sites.

Owing to the rapid development of big data technology, use of machine learning methods to identify soil pollution of potentially contaminated sites (PCS) at regional scales and in different industries has become a research hot spot. However, due to the difficulty in obtaining key indexes of site pollution sources and pathways, current methods have problems such as low accuracy of model predictions and insufficient scientific basis. In this study, we collected the environmental data of 199 PCS in 6 typical industries involving heavy metal and organic pollution. Then, 21 indexes based on basic information, potential for pollution from product and raw material, pollution control level, and migration capacity of soil pollutants were used to established the soil pollution identification index system. We fused the original indexes into the new feature subset with 11 indexes through the method of consolidation calculation. The new feature subset was then used to train machine learning models of random forest (RF), support vector machine (SVM), and multilayer perceptron (MLP), and tested to determine whether it improved the accuracy and precision of soil pollination identification models. The results of correlation analysis showed that the four new indexes created by feature fusion have the correlation with soil pollution is similar to the original indexes. The accuracies and precisions of three machine learning models trained on the new feature subset were 67.4%- 72.9% and 72.0%- 74.7%, which were 2.1%- 2.5% and 0.3%- 5.7% higher than these of the models trained on original indexes, respectively. When the PCS were divided into typical heavy metal and organic pollution sites according to the enterprise industries, the accuracy of the model trained on the two datasets for identifying soil heavy metal and organic pollution were significantly improve to approximately 80%. Owing to the imbalance in positive and negative samples in the prediction of soil organic pollution, the precisions of soil organic pollution identification models were 58%- 72.5%, which were significantly lower than their accuracies. According to the factors analysis based on the model interpretability of SHAP, most of the indexes of basic information, potential for pollution from product and raw material, and pollution control level had different degrees of impact on soil pollution. However, the indexes of migration capacity of soil pollutants had the least effect in the classification task of soil pollution identification of PCS. Among the indexes, traces of soil pollution, industrial utilization years/start-up time, pollution control risk scores and enterprise scale having the greatest effects on soil pollution with the mean SHAP values of 0.17-0.36, which reflected their contribution rate on soil pollution and could help to optimize the current index scoring of the technical regulation for identifying site soil pollution. This study provides a new technical method to identify soil pollution based on big data and machine learning methods, in addition to providing a reference and scientific basis for environmental management and soil pollution control of PCS.

Luminescent ion-doped transparent glass ceramics for mid-infrared light sources [invited].

Glass ceramics (GCs), which consist essentially of a homogeneous solid state dispersion of nanocrystals (NCs) embedded in a chemically inert and mechanically robust glass matrix, appear to be an extremely promising class of solid state materials that can be easily tailored into arbitrary shapes, including a new generation of optical fibers, for efficient incoherent and coherent sources of mid-infrared (MIR) light emission. This unique capability not only stems from the fact that one can tailor the underlying glass matrix for optimal macroscopic physical properties and ultrahigh transparency at the wavelengths of interest (resulting in appropriate "transparent glass ceramics" or TGCs), but also stems from the fact that one can embed these matrices with size and structure-tailored NCs, which in turn can be doped with relatively high concentrations of MIR emitting rare-earth or transition metal ions. This potential is tantamount to the localization of these highly efficient MIR ionic emitters into carefully selected and highly favorable "process-engineered" custom crystalline host "nanocages," while insulating the ionic emitters from the emission-quenching glass host matrix, the latter being chosen largely because of its highly favorable macroscopic bulk properties, including its ductility and formability into near-arbitrary shapes (at appropriate temperatures). Such MIR TGCs appear to be very promising for numerous photonics applications, including compact and relatively efficient waveguide sensors, broadband incoherent MIR light sources, superluminescent light sources, advanced fiber-optic devices, and broadly wavelength-tunable and ultrashort pulse mode-locked fiber and bulk solid-state lasers. In this paper, we review past achievements in this field, starting with an overview of TGCs, followed by discussions of currently preferred methods of fabrication, characterization, and optimization of suitably doped oxyfluoride, tellurite, and chalcogenide TGCs and of our projections of anticipated future developments in this field at both the materials and device levels.

Broadband 2.7 µm mid-infrared emissions in Er3+-doped PbO-PbF2-Bi2O3-Ga2O3 glasses.

Broadband emission at 2.7 µm is observed in an Er3+-doped PbO-PbF2-Bi2O3-Ga2O3 glass. The measured emission band full-width-at-half-maximum (FWHM) is ∼184.4nm, approximately 36 nm wider than that of fluoride glasses. The 2.7 µm emission intensity is almost twice as strong as that of fluoride glasses. The peak values of emission and absorption cross-sections are calculated to be 1.54×10-20cm2 and 1.19×10-20cm2, respectively. This oxyfluoride heavy metal glass shows potential as broadband mid-infrared emission gain material.

Broadband mid-infrared (2.5-5.5 µm) emission from Co2+/Fe2+ codoped chalcogenide glass ceramics.

Compact, mechanically robust, and cost-effective mid-infrared (MIR) light sources are key components in portable and field-deployable gas sensors. Capitalizing on an efficient energy transfer mechanism between Co2+ and Fe2+, we have demonstrated for the first time, to the best of our knowledge, that ultrabroadband 2.5-5.5 µm MIR emission can be achieved at room temperatures in chalcogenide (ChG) glasses that are pumped by a commercially available erbium-doped fiber amplifier emitting at 1.57 µm. These MIR-transparent ChG glass ceramics are embedded with Co2+/Fe2+ codoped ZnSe nanocrystals, and show sufficient MIR emission intensities and bandwidths to enable gas sensing for multiple target analytes such as butane and carbon dioxide. We also describe, to the best of our knowledge, the first observation of a unique "anomalous" increase in the MIR luminescence intensity as a function of temperature.

In-fiber temperature sensor based on green up-conversion luminescence in an Er3+-Yb3+co-doped tellurite glass microsphere.

A novel, to the best of our knowledge, in-fiber temperature sensor based on green up-conversion (UC) luminescence in an Er3+-Yb3+ co-doped tellurite glass microsphere is described. The tellurite glass microsphere is located firmly inside a suspended tri-core hollow-fiber (STCHF) structure. The pump light launched via a single-mode fiber (SMF) is passed through a section of multimode fiber, which is fusion spliced between the SMF and the STCHF into the cores suspended inside the hollow fiber and coupled into the microsphere. Green and red UC emissions of the Er3+ ions are observed using 980 nm pump excitation. The temperature-sensing capability of the tellurite glass microsphere is based on the thermally coupled effect between the upper energy levels responsible for green emissions at 528 nm and 549 nm. The resulting fluorescence intensity ratio, depending on the surrounding temperature range from 303 K to 383 K, is experimentally determined, and a maximum sensitivity of 5.47×10-3 K-1 is demonstrated. This novel in-fiber microsphere-resonator-based device is highly integrated and has the additional advantages of ease of fabrication, compact structure, and low fabrication cost and therefore has great application potential in integrated optical sources including lasers.

Ultrabright single-band red upconversion luminescence in highly transparent fluorosilicate glass ceramics containing KMnF3 perovskite nanocrystals.

With a specially designed composition, highly transparent Yb3+/Er3+-doped fluorosilicate glass ceramic (GC) containing KMnF3 perovskite nanocrystals (NCs) is obtained for the first time. The rare-earth ions are preferentially accumulated in regions embedded with KMnF3 NCs; as a result, a remarkably enhanced (by an order of magnitude) single-band red upconversion luminescence (UCL) is achieved. Absolute quantum efficiency of the red UCL, which cannot be measured in previous GCs owing to insufficiency, reaches as high as 0.10%±0.02% in the GC sample reported in this Letter. This value is even higher than that of the well-known multiband emitting ß-NaYF4:Er3+/Yb3+ NCs and widely recognized GCs containing NaYF4:Yb3+/Er3+NCs.

Comprehensive assessment of health and ecological risk of cadmium in agricultural soils across China: A tiered framework.

Soil cadmium (Cd) contamination has been increasingly serious in agricultural land across China, posing unexpected risks to human health concerning crop safety and terrestrial ecosystems. This study collected Cd concentration data from 3388 soil sites in agricultural regions. To assess the Cd risk to crop safety, a comprehensive sampling investigation was performed to develop reliable Soil Plant Transfer (SPT) model. Eco-toxicity tests with representative soils and organism was conducted to construct the Species Sensitivity Distribution (SSD) for ecological risk assessment. Then, a tiered framework was applied based on Accumulation index, deterministic method (Hazard quotient), and probabilistic assessment (Monte Carlo and Joint Probability Curve). The results revealed the widespread Cd enrichment in agricultural soils, mainly concentrated in Central, Southern, and Southwest China. Risk assessments demonstrated the greater risks related to crop safety, while the ecological risks posed by soil Cd were manageable. Notably, agricultural soils in southern regions of China exhibited more severe risks to both crop safety and soil ecosystem, compared to other agricultural regions. Furthermore, tiered methodology proposed here, can be adapted to other trace elements with potential risks to crop safety and terrestrial ecosystem.

Structure-activity relationships of 6-(aminomethylphenoxy)-benzoxaborole derivatives as anti-inflammatory agent.

A series of novel 6-(aminomethylphenoxy)benzoxaborole analogs was synthesized for the investigation of the structure-activity relationship of the inhibition of TNF-alpha, IL-1beta, and IL-6, from lipopolysaccharide stimulated peripheral blood mononuclear cells. Compounds 9d and 9e showed potent activity against all three cytokines with IC50 values between 33 and 83nM. Chloro substituted analog 9e (AN3485) is considered to be a promising lead for novel anti-inflammatory agent with a favorable pharmacokinetic profile.

Identifying multiple soil pollutions of potentially contaminated sites based on multi-gate mixture-of-experts network.

With the rapid increase in the amount and sources of big data, using big data and machine learning methods to identify site soil pollution has become a research hotspot. However, previous studies that used basic information of sites as pollution identification indexes mainly have problems of low accuracy and efficiency when conducting complex model predictions for multiple soil pollution types. In this study, we collected the environmental data of 199 sites in 6 typical industries involving heavy metal and organic pollution. After feature fusion and selection, 10 indexes based on pollution sources and pathways were used to establish the soil pollution identification index system. The Multi-gate Mixture-of-Experts network (MMoE) were constructed to carry out the multi-tasks of soil heavy metals, VOCs and SVOCs pollution identification simultaneously. The SHAP framework was used to reveal the importance of pollution identification indexes on the multiple outputs of MMoE and obtain their driving factors. The results showed that the accuracies of MMoE model were 0.600, 0.783 and 0.850 for soil heavy metals, VOCs and SVOCs pollution identifications, respectively, which were 0-20 % higher than their accuracies of BP neural networks of single tasks. The indexes of raw material containing organic compounds, enterprise scale, soil pollution traces and industry types have the different significant importance on site soil pollutions. This study proposed a more efficient and accurate method to identify site soil pollutions and their driving factors, which offers a step towards realizing intelligent identification and risk control of site soil pollution globally.

Facilitating Uniform Large-Scale MoS2, WS2 Monolayers, and Their Heterostructures through van der Waals Epitaxy.

The fabrication process for the uniform large-scale MoS2, WS2 transition-metal dichalcogenides (TMDCs) monolayers, and their heterostructures has been developed by van der Waals epitaxy (VdWE) through the reaction of MoCl5 or WCl6 precursors and the reactive gas H2S to form MoS2 or WS2 monolayers, respectively. The heterostructures of MoS2/WS2 or WS2/MoS2 can be easily achieved by changing the precursor from WCl6 to MoCl5 once the WS2 monolayer has been fabricated or switching the precursor from MoCl5 to WCl6 after the MoS2 monolayer has been deposited on the substrate. These VdWE-grown MoS2, WS2 monolayers, and their heterostructures have been successfully deposited on Si wafers with 300 nm SiO2 coating (300 nm SiO2/Si), quartz glass, fused silica, and sapphire substrates using the protocol that we have developed. We have characterized these TMDCs materials with a range of tools/techniques including scanning electron microscopy (SEM), X-ray photoelectron spectroscopy (XPS), micro-Raman analysis, photoluminescence (PL), atomic force microscopy (AFM), transmission electron microscopy (TEM), energy-dispersive X-ray spectroscopy (EDX), and selected-area electron diffraction (SAED). The band alignment and large-scale uniformity of MoS2/WS2 heterostructures have also been evaluated with PL spectroscopy. This process and resulting large-scale MoS2, WS2 monolayers, and their heterostructures have demonstrated promising solutions for the applications in next-generation nanoelectronics, nanophotonics, and quantum technology.

Design and synthesis of boron-containing PDE4 inhibitors using soft-drug strategy for potential dermatologic anti-inflammatory application.

PDE4 inhibitors are a validated approach as anti-inflammatory agents but are limited by systemic side effects including emesis. We report a soft-drug strategy incorporating a carboxylic ester group into boron-containing PDE4 inhibitors leading to the discovery of a series of benzoxaborole compounds with good potency (for example IC(50)=47 nM of compound 2) and low emetic activity. These compounds are intended for dermatological use further limiting possible systemic side effects.

Selective doping of Ni2+ in highly transparent glass-ceramics containing nano-spinels ZnGa2O4 and Zn1+x Ga2-2x Ge x O4 for broadband near-infrared fiber amplifiers.

Selective doping of Ni2+ in octahedral sites provided by nanocrystals embedded in glass-ceramics (GCs) is crucial to the enhancement of broadband near-infrared (NIR) emission. In this work, a NIR emission with a full-width-at-half-maximum (FWHM) of 288 nm is first reported from ZnGa2O4: Ni2+ nano-spinels embedded GCs with excellent transparency. A comparison is made of the NIR luminescence properties of Ni2+ doped GCs containing ZnGa2O4, germanium-substituted ZnGa2O4 nano-spinels (Zn1+x Ga2-2x Ge x O4), and Zn2GeO4/Li2Ge4O9 composite nanocrystals that are free of Ga3+. The results show that ZnGa2O4: Ni2+ GCs exhibit a significantly enhanced NIR emission. The incorporation of the nucleating agent TiO2 is favored in terms of the increased luminescence intensity and prolonged lifetime. The possible causes for the enhancement effect are identified from the crystal structure/defects viewpoint. The newly developed GCs incorporate good reproducibility to allow for a tolerance of thermal treatment temperature and hence hold great potential of fiberization via the recently proposed "melt-in-tube" method. They can be considered as promising candidates for broadband fiber amplifiers.

Modular solid-phase synthetic approach to optimize structural and electronic properties of oligoboronic acid receptors and sensors for the aqueous recognition of oligosaccharides.

This article describes the design and optimization of the first entirely modular, parallel solid-phase synthetic approach for the generation of well-defined polyamine oligoboronic acid receptors and fluorescence sensors for complex oligosaccharides. The synthetic approach allows an effective building of the receptor polyamine backbone, followed by the controlled diversification of the amine benzylic side chains. This approach enabled the testing, in a modular fashion, of the effect of different arylboronic acid units substituted with unencumbering para electron-withdrawing or electron-donating groups. The feasibility of this approach toward automated synthesis was also investigated with the assembly of a sublibrary of receptors by means of the Irori MiniKan technology. Several sublibraries of anthracene-capped sensors containing two or three arylboronic acids were synthesized, and their binding to a series of model disaccharides was examined in neutral aqueous media. The calculation of association constants by fluorescence titrations confirmed that subtle changes in the structures of the interamine spacers in the polyamine backbone can have a significant effect on the stability of the resulting complexes. Most importantly, this study led to the determination of the preferred electronic characteristics for the arylboronate units, and suggests that a new generation of receptors containing very electron-poor arylboronic acids could lead to a significant improvement of binding affinities.

Scandium-catalyzed allylboration of aldehydes as a practical method for highly diastereo- and enantioselective construction of homoallylic alcohols.

A general approach for the allylation of aldehydes using stable, air-tolerant camphor-based chiral allylboronates under Sc(OTf)3 catalysis is described. This practical methodology provides both syn and anti propionate units and other homoallylic alcohols with very high levels of diastereo- and enantioselectivity for several substrates, including functionalized aliphatic aldehydes useful toward the elaboration of complex natural products.