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Fluorescence intensity ratio (FIR)-type optical thermometers based on thermally coupled energy levels (TCLs) of rare earth ions are suitable candidates for noncontact temperature detection in living organisms, microelectronics apparatus, and so forth. Therefore, the improvement of the thermometric sensitivity of TCL-based thermometers has become a research hotspot in recent years. Herein, ultrahigh sensitivity and outstanding resolution for temperature sensing have been realized in YNbO4: Yb3+/Er3+. Unusually, the thermally coupled three-level system of Er3+: 4F7/2/2H11/2/4S3/2is first employed for optical thermometry based on FIR technology. A supernormal thermometric sensitivity of 2.67% K–1is obtained from the thermally coupled 4F7/2and 4S3/2states due to the large energy gap between them, significantly surpassing that of most temperature sensors in the same category. Furthermore, the existence of the intermediate level 2H11/2can effectively prevent the decoupling effect between 4F7/2and 4S3/2. Additionally, the temperature sensing behavior realized by the Stark sublevels of the Er3+: 4I13/2→ 4I15/2transition, with a penetration depth of 8 mm, shows the potential of temperature measurement in deep biological tissues, benefiting from its excitation and emission wavelengths located in the biological window. All of the data reveal that YNbO4: Yb3+/Er3+is an ultrasensitive optical thermometer and exhibits the capacity of temperature detection in deep tissues.

•In murine AML models, Herc1 modulates nucleoside analog resistance and levels of deoxycitidine kinase, their rate-limiting metabolic enzyme.•HERC1is highly expressed in human AML and its elevated expression is associated with worse prognosis.

The solubility of l-PGA in 12 monosolvents was determined by the gravimetric method at temperatures ranging from 283.15 to 328.15 K under atmospheric pressure (101.2 kPa). The solubility values in all of the solvents are positively correlated with the temperature. At 298.15 K, the order of l-PGA solubility was methanol, ethanol, isopropanol, n-propanol, isobutanol, n-butanol, 1,4-dioxane, acetone, 2-butanone, acetonitrile, methyl acetate, and ethyl acetate. The largest solubility is in methanol (5.895 × 10–1at 323.15 K), and the smallest value is observed in ethyl acetate (0.671 × 10–3at 283.15 K). Molecular modeling including the Hirshfeld surface (HS) analysis and molecular electrostatic potential surface (MEPS) was employed to understand internal interactions. Radial distribution function (RDF) analyses indicated a strong correlation between the rank of solute–solvent interactions and the solubility order of l-PGA in the solvents. Additionally, the solubility data were fitted using the modified Apelblat equation, λhequation, NRTL model, and Margules model. The results indicated that the modified Apelblat equation was the most suitable for correlating l-PGA’s solubility. In addition, the NRTL model, known for its high accuracy, was employed to analyze the dissolution thermodynamic characteristics of l-PGA in the selected solvents. The analysis revealed that the dissolution and mixing processes were both spontaneous and entropy-driven.

The coaromatization of light alkane and methanol is an attractive nonpetroleum pathway balancing the heat to produce high-value aromatics, but achieving high p-xylene (pX) selectivity is a challenging topic. Here, we present a two-stage tandem catalysis for alkylation intensification. [Zn,Ga]/ZSM-5 was used in the first-stage reactor for the coaromatization to provide sufficient aromatics. The HZSM-5@SiO2catalysts were prepared for alkylation reaction and characterized by means of X-ray diffraction, N2adsorption/desorption, transmission electron microscopy, and Fourier transform infrared with pyridine. The feasibility to enhance xylene and pX was verified by introducing methanol in the second stage to intensify the alkylation of aromatics. Higher SiO2/Al2O3ratio benefits for xylene and the elimination of external acid sites benefits for pX were found. The pX selectivity and yield and the alkylation efficiency of methanol were significantly increased over HZSM-5@SiO2in the coaromatization system of n-hexane and methanol. The pX selectivity and yield were increased to 57.41% and 13.12 wt % over Z50@20P. Moreover, the deactivation of HZSM-5@SiO2was investigated in detail.

N-Acetyl-l-valine is a crucial pharmaceutical intermediate, and research on the solubility behavior of N-acetyl-l-valine in organic solvents is necessary for processes of crystallization and separation. The solubility data of N-acetyl-l-valine in 12 pure solvents (water, 2-butanone, dimethyl carbonate, n-propanol, isopropanol, n-butanol, isobutanol, acetone, acetonitrile, methyl acetate, ethyl acetate, and 1,4-dioxane) ranging from 283.15 to 323.15 K were measured by the static gravimetric method. Its solubility in all studied solvents increases with the increase of temperature. Moreover, the solubility sequence at 298.15 K is n-propanol > isopropanol > n-butanol > isobutanol > 1,4-dioxane > acetone > 2-butanone > water > methyl acetate > ethyl acetate > acetonitrile > dimethyl carbonate. The solubility behavior is primarily determined by the solvent polarity (ET(30)), hydrogen bonding, and cohesive energy density. Hirshfeld surface analysis (HS) and molecular electrostatic potential surface (MEPS) analysis were used to determine the internal interactions within N-acetyl-l-valine solutions. Four solubility fitting models were used to correlate the experimental mole fraction solubility data, including the modified Apelblat model, the λhequation, the NRTL model, and the UNIQUAC model. Furthermore, the NRTL model was utilized to calculate the mixing thermodynamic characteristics of N-acetyl-l-valine in these solvents. The results indicated that the mixing process was entropy-driven and spontaneous.

Photodetectors that are sensitive in the shortwave-infrared (SWIR) range (1–2 µm) are of great interest for applications such as machine vision, autonomous driving and three-dimensional, night and adverse weather imaging, among others. Currently available technologies in the SWIR range rely on costly epitaxial semiconductors that are not monolithically integrated with complementary metal–oxide–semiconductor electronics. Solution-processed quantum dots can address this challenge by enabling low-cost manufacturing and simple monolithic integration on silicon in a back-end-of-line process. So far, colloidal quantum dot materials to access the SWIR regime are mostly based on lead sulfide and mercury telluride compounds, imposing major regulatory concerns for their deployment in consumer electronics due to the presence of toxic heavy metals. Here we report a new synthesis method for environmentally friendly silver telluride quantum dots and their application in high-performance SWIR photodetectors. The colloidal quantum dot photodetector stack employs materials compliant with the Restriction of Hazardous Substances directives and is sensitive in the spectral range from 350 nm to 1,600 nm. The room-temperature detectivity is of the order of 1012Jones, the 3 dB bandwidth is in excess of 0.1 MHz and the linear dynamic range is over 118 dB. We also realize a monolithically integrated SWIR imager based on solution-processed, toxic-heavy-metal-free materials, thus paving the way for this technology to the consumer electronics market.

Autistic spectrum disorder (ASD) is a male-biased, heterogeneous neurodevelopmental disorder that affects approximately 1%–2% of the population. Prenatal exposure to valproic acid (VPA) is a recognized risk factor for ASD, but the cellular and molecular basis of VPA-induced ASD at the single-cell resolution is unclear. Here, we aim to compare the cellular and molecular differences in the hippocampus between male and female prenatal mice with ASD at the single-cell transcriptomic level. The transcriptomes of more than 45,000 cells are assigned to 12 major cell types, including neurons, glial cells, vascular cells, and immune cells. Cell type-specific genes with altered expression after prenatal VPA exposure are analyzed, and the largest number of differentially expressed genes (DEGs) are found in neurons, choroid plexus epithelial cells, and microglia. In microglia, several pathways related to inflammation are found in both males and females, including the tumor necrosis factor (TNF), nuclear factor kappa B (NF-κB), toll-like receptor (TLR), and mitogen-activated protein kinase (MAPK) signaling pathways, which are important for the induction of autistic-like behavior. Additionally, we note that several X-linked genes, including Bex1, Bex3, and Gria3, were among the male-specific DEGs of neurons. This pioneering study describes the landscape of the transcriptome in the hippocampus of autistic mice. The elucidation of sexual differences could provide innovative strategies for the prevention and treatment of ASD.

Colloidal quantum dot (CQD) technology is considered the main contender toward a low-cost high-performance optoelectronic technology platform for applications in the short-wave infrared (SWIR) to enable 3D imaging, LIDAR night vision, etc. in the consumer electronics and automotive markets. In order to unleash the full potential of this technology, there is a need for a material that is environmentally friendly, thus RoHS compliant, and possesses adequate optoelectronic properties to deliver high-performance devices. InSb CQDs hold great potential in view of their RoHS-compliant nature and─in principle─facile access to the SWIR. However, to date progress in realizing high-performance optoelectronic devices, including photodetectors (PDs), has been limited. Here, we have developed a synthesis method for producing size-tunable InSb CQDs with distinct excitonic peaks spanning a wide range from 900 to 1750 nm. To passivate the surface defects and enhance the photoluminescence (PL) efficiency of InSb CQDs, we further designed an InSb/InP core–shell structure. By employing the InSb/InP core–shell CQDs in a photodiode device stack, we report on robust InSb CQD SWIR photodetectors that exhibit an external quantum efficiency (EQE) of 25% at 1240 nm, a wide linear dynamic range exceeding 128 dB, a photoresponse time of 70 ns, and a specific detectivity of 4.4 × 1011jones.

Chiral lead halide perovskites (LHPs) have been widely investigated in chiroptical spintronics due to their significant Rashba spin–orbit coupling (SOC) and chiral-induced spin selectivity (CISS). Ferromagnet/LHP spinterface stems from the orbital hybridization at the interface of the ferromagnet and the nonmagnetic semiconductor, where interfacial density of state is spin-dependent. By far, the impact of the ferromagnet/chiral LHP spinterface on magneto-photoluminescence (Magneto-PL) of chiral LHPs remains unknown. In this work, we find that the negative and tunable Magneto-PL effects for the pristine LHP bulk film can be drastically enhanced by incorporating ferromagnetic/chiral LHP interfaces. A large Magneto-PL magnitude can reach approximately −13% for the Ni/(S-MBA)2PbI4interface at the field strengths of ±900 mT. With the assistance of circularly polarized PL spectra, anisotropic magneto-resistance, and X-ray photoelectron spectroscopy measurements, we demonstrate that the ferromagnet/chiral LHP interfaces are chirality/spin-dependent and possess ferromagnetic property due to distinct magnetic switching behavior and electronic orbit coupling at interfaces, which boost the Rashba splitting and spin mixing. The comprehensive effects of Rashba-induced exciton states and chiral-induced SOC at chiral spinterfaces with CISS are responsible for the enhanced Magneto-PL of Ni/(R/S-MBA)2PbI4. It is postulated that the chiral spinterfaces play a dominant role for achieving large and tunable magneto-optical effect of chiral LHPs. This work paves the way for chiroptical spintronic applications.

In this study, Methane dehydrogenation aromatization (MDA) over the 1Zn3Pt/Al2O3-6Mo/HZSM-5 physically mixed catalyst overcame the thermodynamic limitations through an efficient two-step cascade reaction at 550 °C and atmospheric pressure. Meanwhile, MDA reaction realized long-term stability during 30 h with methane conversion of 2.6% and as high as 98% selectivity of aromatics. A series of characterizations of this bifunctional physically mixed catalyst proved that the bifunctional physically mixed catalyst inhibits the coke deposition induced by over-aromatization and deep dehydrogenation, and the added Zn promoter promotes hydrogen spillover on Pt metal and inhibits further the sintering of Pt particles, contributing to the stable cascade conversion of methane to aromatics at low temperatures.

In the future driverless scenario, pedestrian-vehicle conflict is an unavoidable traffic problem at unsignalized mid-block crosswalks. Autonomous vehicles are obviously impossible to make eye contact with pedestrians the way a human driver can. Therefore, there is an urgent need for autonomous vehicles to achieve accurate predictions of pedestrian crossing behavior. In order to better solve the problem of high uncertainty of pedestrian crossing behavior, in this paper, a modeling framework combines Maximum Entropy Deep Inverse Reinforcement Learning (Deep MEIRL) and reinforcement learning is employed to predict pedestrian crossing behaviors. The dataset of drone-based video footage is collected in Xi’an (China) to train and validate the model. The trajectory dataset, extracted by computer vision algorithm, is implemented to derive state features of pedestrian behavior, including pedestrian to target area distance, vehicle type, vehicle speed, lateral/longitudinal distances, pedestrian lateral/longitudinal velocities. The results reveal that Deep MEIRL performs better than a baseline model MEIRL at micro-scales such as predicting pedestrian trajectories and evasive actions. Specifically, in predicting pedestrian trajectories, the Deep MEIRL outperforms the MEIRL by 33.09% and 15.16% on the basis of the MAE and HD, respectively. Meanwhile, the Deep MEIRL is 28.7% and 17.6% respectively more accurate than the MEIRL in predicting pedestrian evasive actions on lateral and longitudinal directions. Furthermore, we also found that there is heterogeneity in pedestrian crossing behavior when interacting with different vehicles. This research can contribute to a critical step toward addressing the safe and efficient movement at unsignalized mid-block crosswalks for autonomous vehicles.

Achieving low-threshold infrared stimulated emission in solution-processed quantum dots is critical to enable real-life applications including photonic integrated circuits (PICs), LIDAR application, and optical telecommunication. However, realization of low threshold infrared gain is fundamentally challenging due to high degeneracy of the first emissive state (e.g., 8-fold) and fast Auger recombination. In this Letter, we demonstrate ultra-low-threshold infrared stimulated emission with an onset of 110 μJ cm–2employing cascade charge transfer (CT) in Pb-chalcogenide colloidal quantum dot (CQD) solids. In doing so, we investigate this idea in two different architectures including a mixture of multiband gap CQDs and a layer-by-layer (LBL) configuration. Using transient absorption spectroscopy, we show ultrafast cascade CT from large band gap PbS CQD to small band gap PbS/PbSSe core/shell CQDs in LBL (∼2 ps) and mixture (∼9 ps) configurations. These results indicate the feasibility of using cascade CT as an efficient method to reduce the optical gain threshold in CQD solid films.

The solubility data of l-valine were measured by the static gravimetric method in 12 pure solvents (water, methanol, acetonitrile, 1,4-dioxane, n-propanol, isopropanol, ethanol, acetone, methyl acetate, ethyl acetate, n-butanol, and isobutanol) at 283.15–323.15 K (except for 1,4-dioxane at 288.15–328.15 K) and 101.2 kPa. The increasing temperature shows a positive effect on l-valine solubility in all the solvents. In addition, the solubility sequence of l-valine at 298.15 K is water > methanol > ethanol >1,4-dioxane > isobutanol > acetone > n-butanol > ethyl acetate > acetonitrile > methyl acetate > isopropanol > n-propanol. The polymorphism of l-valine was investigated by the powder X-ray diffraction test, and the patterns show that there is no crystal transformation in the process of solubility measurement. The main factors influencing the solubility behavior include solvent polarity (ET(30)), hydrogen bond, cohesive energy density, and Hansen solubility parameters (HSPs). Two mathematical models, i.e., the modified Apelblat model and the Yaws model, were applied to correlate the measured solubility data. The ARDs, RMSDs, AIC, and Akaike weights were calculated to evaluate the fitting results.

A gravimetric method is adopted to measure the solid–liquid equilibrium of N1,N3,N5-tris(pyridin-4-yl)benzene-1,3,5-tricarboxamide (TPBTC), a hydrogen-bonded organic framework (HOF) monomer in different solvents (methanol, dichloromethane, isopropanol, n-hexane, acetone, ethanol, 2-butanone, n-butanol, acetonitrile, water, ethyl acetate, and n-propanol). The experiment is carried out at atmospheric pressure, and the experimental temperatures range from 283.15 to 323.15 K. The powder X-ray diffraction analysis was performed on the equilibrium solid-phase characterization of TPBTC in different solvents, and the solid form of solute in equilibrium with different solvents was a mixture of three kinds of crystalline forms and an amorphous form. In the 12 monosolvents, the solubility increased with absolute temperature, and the following order was obtained: 2-butanone > isopropanol > n-propanol > n-butanol > ethyl acetate > acetonitrile > methanol > ethanol > dichloromethane > n-hexane > acetone > water. In the studied temperature range, the maximum and minimum values were recorded for 2-butanone (0.6753 × 10–3mol/mol at 323.15 K) and water (0.004650 × 10–3mol/mol at 283.15 K), respectively. The solubility behavior and solvent effects in various solvents were first investigated by the hydrogen bond acceptor tendencies as the main factor then interpreted by solvent polarity, cohesive energy density, and steric effect for some exceptions. In addition, the solid–liquid equilibrium data of TPBTC were correlated with the Yaws model and the Apelblat model. Furthermore, the two thermodynamic models were evaluated using the Akaike Information Criterion and Akaike weights. It can be observed that the Yaws model was more appropriate than the Apelblat model.

Nanofibrillated cellulose paper (nanopaper) has gained growing interest as one promising substrate material for paper-based microfluidics, thanks to its ultrasmooth surface, high optical transparency, uniform nanofiber matrix with nanoscale porosity, and tunable chemical properties. Recently, research on nanopaper-based microfluidics has quickly advanced; however, the current technique of patterning microchannels on nanopaper (i.e., 3D printing, spray coating, or manual cutting and sticking), that is fundamental for application development, still has some limitations, such as ease-of-contamination, and more importantly, only enabling millimeter-scale channels. This paper reports a facile process that leverages the simple operations of microembossing with the convenient plastic micro-molds, for the first time, patterning nanopaper microchannels downing to 200 μm, which is 4 times better than the existing methods and is time-saving (<45 mins). We also optimized the patterning parameters and provided one quick look-up table as the guideline for application developments. As proof-of-concept, we first demonstrated two fundamental microfluidic devices on nanopaper, the laminar-mixer and droplet generator, and two functional nanopaper-based analytical devices (NanoPADs) for glucose and Rhodamine B (RhB) sensing based on optical colorimetry and surface-enhanced Raman spectroscopy, respectively. The two NanoPADs showed outstanding performance with low limits of detection (2 mM for glucose and 19fM for RhB), which are 1.25× and 500× fold improvement compared to the previously reported values. This can be attributed to our newly developed highly accurate microchannel patterning process that enables high integration and fine-tunability of the NanoPADs along with the superior optical properties of nanopaper.

Exploring pancreatic ductal adenocarcinoma (PDAC) metabolic landscape would contribute to further understand PDAC from the metabolic perspective and provide more details for precise treatment design. This study aims to describe metabolic landscape of PDAC. Bioinformatics analysis was used to investigate the differences of genome, transcriptome, and proteome levels of metabolic patterns. Three subtypes (MC1, MC2, and MC3) were identified and characterized as distinct metabolic patterns. MC1, enriched in lipid metabolism and amino acid metabolism signatures, was associated with lower abundance of immune cells and stromal cells, and non-response to immunotherapy. MC2 displayed immune-activated characteristics, minor genome alterations and good response to immunotherapy. MC3 was characterized by high glucose metabolism, high pathological grade, immune-suppressed features, poor prognosis, and epithelial-mesenchymal transition phenotype. A ninety-three gene classifier preformed robust prediction and high accuracy (training set: 93.7%; validation set 1: 85.0%; validation set 2: 83.9%). Using random forest classifier, probabilities of three patterns could be predicted on pancreatic cancer cell lines, which could be used to find vulnerable targets in response to both genetic and drug perturbation. Our study revealed features of PDAC metabolic landscape, which could be expected to provide a reference for prognosis prediction and precise treatment design.

Defect engineering can offer active sites for N2adsorption and activation due to the abundant localized electrons of surface defects. However, the high energy barriers of the defect band can restrict the electron transfer, which thus limits the photocatalytic N2fixation performance. Herein, we demonstrate that Ru/W18O49exhibits a nearly 6-fold enhanced photocatalytic activity for ammonia production under visible irradiation compared with pristine W18O49. Our investigations reveal that the enhanced photocatalytic N2fixation activities are mainly attributed to the synergistic effect of oxygen vacancies and Ru modification. The decorated Ru species can modify the coordination structure of oxygen vacancies by forming Ru–O–W bonds. The Ru–O–W centers can serve as active sites for the side-on adsorption of N2due to their asymmetric polarization, better promoting the electron transfer to absorbed N2. Moreover, the LSPR between Ru and oxygen vacancies provides sufficient electrons for N2activation. As a result, the efficient electron transfer facilitates the activation and dissociation of N2on Ru/W18O49, which is feasible for the reduction of N2to NH3. This work provides a promising strategy for enhanced photocatalytic N2fixation by modulating defect engineering.

The crossing behaviors modeling of non-networked road users can improve Connected and Autonomous Vehicles (CAVs)’s awareness of the imminent hazards in shared space while planning routes. In this study, an agent-based microsimulation model is utilized to simulate the crossing behavior of e-bikes which is difficult to model due to their greater maneuverability. To deeply understand the crossing mechanism of e-bikes, a customized neural network-based nonlinear reward function is developed from five dimensions, including travel efficiency, travel direction, travel destination, risk avoidance, and travel location. A Deep Maximum Entropy Inverse Reinforcement Learning(Deep MEIRL), which can recover the nonlinear reward function, is introduced to predict trajectories of e-bikes from the real drone-based video dataset, collected at the intersection of Jixiangcun, Xi’an (China). The results reveal that the Deep MEIRL can simulate the e-bike crossing trajectory more precisely, particularly in the microscopic behaviors of riders. Comparing Deep MEIRL with the baseline model MEIRL, it can be found that the nonlinear reward function designed in this paper is more advantageous in terms of continuous space modeling, with an improvement of 19.77%. Notably, Deep MEIRL outperforms MEIRL in modeling distance to potentially risk target states for left- and right-turn crossing behavior, improving Mean Absolute Error (MAE) by 71% and 30%, respectively. It means that the Deep MEIRL with a designed neural network is of great value to provide a logical result in modeling e-bike interaction with other road users. Therefore, the research is conducive to CAVs to understand e-bike behaviors in complex traffic scenarios, thereby assisting CAVs in making decisions efficiently.

Forward and backward reaching inverse kinematics (FABRIK) is an efficient two-stage iterative solver for inverse kinematics of spherical-joint manipulator without the calculation of Jacobian matrix. Based on FABRIK, this paper presents an incremental control scheme for a free-floating space manipulator consists of revolute joints and rigid links with the consideration of joint constraints and dynamic coupling effect. Due to the characteristics of FABRIK, it can induce large angular movements on specific joints. Apart from that, FABRIK maps three dimensional (3D) problem into two dimensional (2D) problem by a simple geometric projection. This operation can cause infinite loops in some cases. In order to overcome these issues and apply FABRIK on space manipulators, an increments allocation method is developed to constrain the angular movements as well as to re-orient the end-effector. The manipulator is re-positioned based on the momentum conservation law. Instead of pure target position tracking, the orientation control of the end-effector is also considered. Numerical simulation is performed to testify and demonstrate the effectiveness and reliability of the proposed incremental control approach.

Prussian blue analogues (PBA), common metal-organic frameworks (MOFs), are typically binary metal compounds. In this study, chromium was incorporated during the preparation of nickel‑cobalt PBA, resulting in the adjustment of the micro-morphology of PBA and the synthesis of small-sized PBA (sPBA) electrode materials with diameters ranging from 20 to 30 nm. These materials underwent various interfacial modifications to yield ternary metal phosphide (sPBA-P) and hollow nanostructures (sPBA-C). Additionally, by integrating graphene oxide (GO) as the growth substrate, successful preparation of GO@sPBA-P and GO@sPBA-C electrode materials was achieved. Significantly, these materials showcase outstanding electrochemical properties and interesting interfacial reactions when employed as cathode and anode components. Through a comprehensive series of characterization analyses, the electrochemical properties and reaction mechanisms of these materials were thoroughly investigated. Ultimately, an asymmetric supercapacitor (ASC) was assembled using the synthesized GO@sPBA-P and GO@sPBA-C to explore the material's potential in energy materials applications.