Furfuryl Alcohol Emulsion Resins as Co-Binders for Urea-Formaldehyde Resin-Bonded Particleboards

An approach to using water-insoluble furfuryl alcohol (FA) resins as a co-binder for particleboard (PB) urea-formaldehyde (UF) resins was evaluated. Sprayable FA/UF mixed resins were made by emulsifying FA resins of varying advancements and mixing with various formaldehyde to urea (F/U) ratio UF resins in various proportions. The binder performance of the mixed FA/UF resins was then evaluated by bonding laboratory PBs using a weakly acidic ordinary UF resin curing catalyst at various hot pressing temperatures. The PBs were also heat-treated and were aged for two years at room temperature. The test results of bond strengths and formaldehyde emission levels of PBs showed promising improvements at about 30% FA resin additions, although the results were preliminary due to the variable performance nature of such binder systems

Mississippi Sky Conditions

This dataset consists of approximately 13,000 jpg format images. These images were collected using consumer grade trail cameras manufactured by Browning Trail Cameras. Cameras were installed across Mississippi (USA) in 2019 and 2020 from March through September. Images collected are exclusively oblique, unobstructed views of the sky. Cameras were placed in time-lapse mode and set to collect one image every hour. Our intent in this work was to first compare deep learning approaches to classify sky conditions with regard to cloud shadows in agricultural fields using a visible spectrum camera. Sky conditions, and specifically shadowing from clouds, are critical determinants in the quality of images that can be obtained from low-altitude sensing platforms. Radiometric quality of remotely sensed imagery is crucial for precision agriculture applications because estimations of plant health rely on the underlying quality. Using this dataset, we then developed an artificial-intelligence-based edge computing system to fully automate the classification process

Real-Time Automated Classification of Sky Conditions Using Deep Learning and Edge Computing

The radiometric quality of remotely sensed imagery is crucial for precision agriculture applications because estimations of plant health rely on the underlying quality. Sky conditions, and specifically shadowing from clouds, are critical determinants in the quality of images that can be obtained from low-altitude sensing platforms. In this work, we first compare common deep learning approaches to classify sky conditions with regard to cloud shadows in agricultural fields using a visible spectrum camera. We then develop an artificial-intelligence-based edge computing system to fully automate the classification process. Training data consisting of 100 oblique angle images of the sky were provided to a convolutional neural network and two deep residual neural networks (ResNet18 and ResNet34) to facilitate learning two classes, namely (1) good image quality expected, and (2) degraded image quality expected. The expectation of quality stemmed from the sky condition (i.e., density, coverage, and thickness of clouds) present at the time of the image capture. These networks were tested using a set of 13,000 images. Our results demonstrated that ResNet18 and ResNet34 classifiers produced better classification accuracy when compared to a convolutional neural network classifier. The best overall accuracy was obtained by ResNet34, which was 92% accurate, with a Kappa statistic of 0.77. These results demonstrate a low-cost solution to quality control for future autonomous farming systems that will operate without human intervention and supervision

Isolating the Role of the Node-Linker Bond in the Compression of UiO-66 Metal-Organic Frameworks

Understanding the mechanical properties of metal–organic frameworks (MOFs) is essential to the fundamental advancement and practical implementations of porous materials. Recent computational and experimental efforts have revealed correlations between mechanical properties and pore size, topology, and defect density. These results demonstrate the important role of the organic linker in the response of these materials to physical stresses. However, the impact of the coordination bond between the inorganic node and organic linker on the mechanical stability of MOFs has not been thoroughly studied. Here, we isolate the role of this node–linker coordination bond to systematically study the effect it plays in the compression of a series of isostructural MOFs, M-UiO-66 (M = Zr, Hf, or Ce). The bulk modulus (i.e. the resistance to compression under hydrostatic pressure) of each MOF is determined by in situ diamond anvil cell (DAC) powder X-ray diffraction measurements and density functional theory (DFT) simulations. These experiments reveal distinctive behavior of Ce-UiO-66 in response to pressures under one GPa. In situ DAC Raman spectroscopy and DFT calculations support the observed differences in compressibility between Zr-UiO-66 and the Ce- analogue. Monitoring changes in bond lengths as a function of pressure through DFT simulations provides a clear picture of those which shorten more drastically under pressure and those which resist compression. This study demonstrates that changes to the node–linker bond can have significant ramifications on the mechanical properties of MOFs

Transient Catenation in a Zirconium-Based Metal–Organic Framework and Its Effect on Mechanical Stability and Sorption Properties

International audienceInterpenetration of two or more sublattices is common among many metal–organic frameworks (MOFs). Herein, we study the evolution of one zirconium cluster-based, 3,8-connected MOF from its non-interpenetrated (NU-1200) to interpenetrated (STA-26) isomer. We observe this transient catenation process indirectly using ensemble methods, such as nitrogen porosimetry and X-ray diffraction, and directly, using high-resolution transmission electron microscopy. The approach detailed here will serve as a template for other researchers to monitor the interpenetration of their MOF samples at the bulk and single-particle limits. We investigate the mechanical stability of both lattices experimentally by pressurized in situ X-ray diffraction and nanoindentation as well as computationally with density functional theory calculations. Both lines of study reveal that STA-26 is considerably more mechanically stable than NU-1200. We conclude this study by demonstrating the potential of these MOFs and their mixed phases for the capture of gaseous n-hexane, used as a structural mimic for the chemical warfare agent sulfur mustard gas