Ultra-Robust Flexible Electronics by Laser-Driven Polymer-Nanomaterials Integration

Polyethylene terephthalate (PET) is the most widely used polymer in the world. For the first time, the laser-driven integration of aluminum nanoparticles (Al NPs) into PET to realize a laser-induced graphene/Al NPs/polymer composite, which demonstrates excellent toughness and high electrical conductivity with the formation of aluminum carbide into the polymer is shown. The conductive structures show an impressive mechanical resistance against >10000 bending cycles, projectile impact, hammering, abrasion, and structural and chemical stability when in contact with different solvents (ethanol, water, and aqueous electrolytes). Devices including thermal heaters, carbon electrodes for energy storage, electrochemical and bending sensors show this technology's practical application for ultra-robust polymer electronics. This laser-based technology can be extended to integrating other nanomaterials and create hybrid graphene-based structures with excellent properties in a wide range of flexible electronics’ applications.     

Forging furnace with thermochemical waste-heat recuperation by natural gas reforming: Fuel saving and heat balance

This paper considers thermochemical recuperation (TCR) of waste-heat using natural gas reforming by steam and combustion products. Combustion products contain steam (H₂O), carbon dioxide (CO₂), and ballast nitrogen (N₂). Because endothermic chemical reactions take place, methane steam-dry reforming creates new synthetic fuel that contains valuable combustion components: hydrogen (H₂), carbon monoxide (CO), and unreformed methane (CH₄). There are several advantages to performing TCR in the industrial furnaces: high energy efficiency, high regeneration rate (rate of waste-heat recovery), and low emission of greenhouse gases (CO₂, NO_x). As will be shown, the use of TCR is significantly increasing the efficiency of industrial furnaces – it has been observed that TCR is capable of reducing fuel consumption by nearly 25%. Additionally, increased energy efficiency has a beneficial effect on the environment as it leads to a reduction in greenhouse gas emissions.     

Learning and Predicting Photonic Responses of Plasmonic Nanoparticle Assemblies via Dual Variational Autoencoders

The application of machine learning is demonstrated for rapid and accurate extraction of plasmonic particles cluster geometries from hyperspectral image data via a dual variational autoencoder (dual-VAE). In this approach, the information is shared between the latent spaces of two VAEs acting on the particle shape data and spectral data, respectively, but enforcing a common encoding on the shape-spectra pairs. It is shown that this approach can establish the relationship between the geometric characteristics of nanoparticles and their far-field photonic responses, demonstrating that hyperspectral darkfield microscopy can be used to accurately predict the geometry (number of particles, arrangement) of a multiparticle assemblies below the diffraction limit in an automated fashion with high fidelity (for monomers (0.96), dimers (0.86), and trimers (0.58). This approach of building structure-property relationships via shared encoding is universal and should have applications to a broader range of materials science and physics problems in imaging of both molecular and nanomaterial systems.     

MidDRIFTS-PLSR-based quantification of physico-chemical soil properties across two agroecological zones in Southwest Germany: generic independent validation surpasses region specific cross-validation

Development of spectroscopic prediction models via partial least squares regression (PLSR) suggests that model performance is highly affected by means of calibration and nature of the dataset. This study compares the predictive performance of PLSR models obtained by cross-validation and independent validation to quantify physico-chemical soil properties from their mid-infrared diffuse reflectance Fourier transform spectra (midDRIFTS) across two contrasting regions, Kraichgau (K) and Swabian Alb (SA), in Southwest Germany. We evaluated the capability of midDRIFTS-PLSR models for predicting total carbon (TC), organic carbon (TOC), inorganic carbon (TIC), nitrogen (TN), mineral N (N_min), C:N ratio, hot water extractable C and N (C_HWE, N_HWE), microbial biomass C and N (C_mic, N_mic), pH, bulk density, and clay, silt and sand contents of 126 soil samples. Based on calibrated models, most soil properties were predicted successfully using either calibration approach with residual prediction deviations ≥3 and R² > 0.9, except for N_min, C/N ratio, pH, bulk density and sand. However, predictive performance of generic independent validation derived models (GIC) of test set was considerably higher than generic cross-validation models. Validation using GIC models gave relatively the same predictive performance with those obtained in calibration except for N_min. Validation of region specific cross-validated models, however, resulted in successful predictions only for TC, TIC, TOC and TN in SA and TC and TIC and TOC in K. Our results show the superiority of independent validation over both generic and region specific cross-validation as a robust tool for predicting soil properties without further laboratory measurements.     

Uncovering the Potential of M1-Site-Activated NASICON Cathodes for Zn-Ion Batteries

There is a long-standing consciousness that the rhombohedral NASICON-type compounds as promising cathodes for Li⁺/Na⁺ batteries should have inactive M1(6b) sites with ion (de)intercalation occurring only in the M2 (18e) sites. Of particular significance is that M1 sites active for charge/discharge are commonly considered undesirable because the ion diffusion tends to be disrupted by the irregular occupation of channels, which accelerates the deterioration of battery. However, it is found that the structural stability can be substantially improved by the mixed occupation of Na⁺/Zn²⁺ at both M1 and M2 when using NaV₂(PO₄)₃ (NVP) as a cathode for Zn-ion batteries. The results of atomic-scale scanning transmission electron microscopy, analysis of ab initio molecular dynamics simulations, and an accurate bond-valence-based structural model reveal that the improvement is due to the facile migration of Zn²⁺ in NVP, which is enabled by a concerted Na⁺/Zn²⁺ transfer mechanism. In addition, significant improvement of the electronic conductivity and mechanical properties is achieved in Zn²⁺-intercalated ZnNaV₂(PO₄)₃ in comparison with those of Na₃V₂(PO₄)₃. This work not only provides in-depth insight into Zn²⁺ intercalation and dynamics in NVP unlocked by activating the M1 sites, but also opens a new route toward design of improved NASICON cathodes.     

Non-homogeneous extracellular resistivity affects the current-source density profiles of up-down state oscillations

Rhythmic local field potential (LFP) oscillations observed during deep sleep are the result of synchronized electrical activities of large neuronal ensembles, which consist of alternating periods of activity and silence, termed 'up' and 'down' states, respectively. Current-source density (CSD) analysis indicates that the up states of these slow oscillations are associated with current sources in superficial cortical layers and sinks in deep layers, while the down states display the opposite pattern of source-sink distribution. We show here that a network model of up and down states displays this CSD profile only if a frequency-filtering extracellular medium is assumed. When frequency filtering was modelled as inhomogeneous conductivity, this simple model had considerably more power in slow frequencies, resulting in significant differences in LFP and CSD profiles compared with the constant-resistivity model. These results suggest that the frequency-filtering properties of extracellular media may have important consequences for the interpretation of the results of CSD analysis.     

Self-propagating high-temperature synthesis of ferrovanadium nitride for use in smelting high-strength low-alloy steels

A new alloying material for smelting high-strength low-alloy steel is considered: FERVANIT fused ferrovanadium nitride. Self-propagating high-temperature synthesis in the ferrovanadium-nitrogen system permits the development of a new industrial production technology for alloys based on vanadium nitride. Self-propagating high-temperature synthesis requires no electrical energy, is environmentally benign, and results in a product with good operational properties.     

遥控钥匙门禁系统的路径损耗

在遥控钥匙门禁(RKE)系统中,可以用钥匙扣上的发射器从远端开锁,发射器将无线编码发送到汽车内的接收机。遥控钥匙门禁(RKE)系统通常工作在ISM频段,包括315MHz和433．92MHz。随着远程启动和带校验的RKE的出现,设计     

The incorporation of mono- and bi-functionalized multiwall carbon nanotubes in organic photovoltaic cells

We have successfully prepared mono- and bi-functionalized multiwall carbon nanotubes (MWCNT) with thiophene, amine and thiophene-amine groups. The dispersion of nanotubes has been enhanced and stable optimized dispersions in organic solvents were obtained. These functionalized nanotubes have been successfully incorporated into bulk heterojunction (BHJ) organic photovoltaic (OPV) cells with a poly (3-hexyl thiophene) (P3HT) and [6, 6]-phenyl-C(61)-butyric acid methyl ester (PCBM) photoactive blended layer. The incorporation of MWCNT with different functional groups, in the active layer, results in different cell performance with respect to a reference cell. A maximum power conversion efficiency of 2.5% is achieved with the inclusion of thiophene functionalized nanotubes. This improvement in the device performance is attributed to an extension of the exciton dissociation volume and charge transport properties through the nanotube percolation network in P3HT/CNT, PCBM/CNT or both phases. This is believed to be due to more efficient dispersion of the functionalized nanotubes within the photoactive composite layer.