Metribuzin, a widely used herbicide and a frequently detected pollutant in the environment, was studied as a target compound for membrane anodic Fenton treatment (AFT), a Fenton technology with application potential for on-site treatment of pesticide wastewater. It was found that the degradation kinetics of metribuzin do not obey the AFT model, a previously developed model that fit AFT degradation kinetics of all previously investigated pesticides. The lack of fit for metribuzin data was determined to result from a weak interaction between metribuzin and the ferric ion, resulting in a significant reduction in availability of metribuzin for reaction with hydroxyl radicals during AFT, thus slowing degradation. A revised kinetic model was developed based on the original AFT model with the addition of this interaction. Results demonstrate that the new kinetic model fits metribuzin degradation data quite well at different delivery rates of Fenton reagent and at different temperatures. This weak interaction is also found to exist between ferric ion and several other triazinone/triazine herbicides during membrane AFT. The interaction intensity correlates with the electron-withdrawing/-donating property of substituents on the triazine/triazinone ring. The stronger the electron-donating ability of substituents, the stronger the interaction.

Using atomistic simulations, we demonstrate that β-sheet, an essential component of spider silk protein, has a thermal conductivity 1-2 orders of magnitude higher than that of some other protein structures reported in the literature. In contrast to several other nanostructured materials of similar bundled/layered structures (e.g. few-layer graphene and bundled carbon nanotubes), the β-sheet is found to uniquely feature enhanced thermal conductivity with an increased number of constituting units, i.e. β-strands. Phonon analysis identifies inter-β-strand hydrogen bonding as the main contributor to the intriguing phenomenon, which prominently influences the state of phonons in both low- and high-frequency regimes. A thermal resistance model further verifies the critical role of hydrogen bonding in thermal conduction through β-sheet structures.

[1] In recent years, applications of distributed temperature sensing (DTS) have increased in number and diversity. Because fiber-optic cables used for DTS are typically sheathed in dark UV-resistant materials, the question arises as to how shortwave solar radiation penetrating a water column influences the accuracy of absolute DTS-derived temperatures in aquatic applications. To quantify these effects, we completed a modeling effort that accounts for the effects of radiation and convection on a submersed cable to predict when solar heating may be important. Results indicate that for cables installed at shallow depths in clear, low-velocity water bodies, measurable heating of the cable is likely during peak solar radiation. However, at higher velocities, increased turbidity and/or greater depths, the effects of solar heating are immeasurable. A field study illustrated the effects of solar radiation by installing two types of fiber-optic cable at multiple water depths (from 0.05 to 0.8 m) in the center and along the sidewall of a trapezoidal canal. Thermistors were installed at similar depths and shielded from solar radiation to record absolute water temperatures. During peak radiation, thermistor data showed small temperature differences (∼0.003°C-0.04°C) between depths suggesting minor thermal stratification in the canal center. DTS data from cables at these same depths show differences of 0.01°C-0.17°C. The DTS differences cannot be explained by stratification alone and are likely evidence of additional heating from solar radiation. Sidewall thermistor strings also recorded stratification. However, corresponding DTS data suggested that bed conduction overwhelmed the effects of solar radiation.

Articles you may be interested in Fluorescence resonance energy transfer measured by spatial photon migration in CdSe-ZnS quantum dots colloidal systems as a function of concentration Appl. Phys. Lett. 105, 203108 (2014) As a system of interest gets small, due to the influence of the sensor mass and heat leaks through the sensor contacts, thermal characterization by means of contact temperature measurements becomes cumbersome. Non-contact temperature measurement offers a suitable alternative, provided a reliable relationship between the temperature and the detected signal is available. In this work, exploiting the temperature dependence of their fluorescence spectrum, the use of quantum dots as thermomarkers on the surface of a fiber of interest is demonstrated. The performance is assessed of a series of neural networks that use different spectral shape characteristics as inputs (peak-based-peak intensity, peak wavelength; shape-based-integrated intensity, their ratio, full-width half maximum, peak normalized intensity at certain wavelengths, and summation of intensity over several spectral bands) and that yield at their output the fiber temperature in the optically probed area on a spider silk fiber. Starting from neural networks trained on fluorescence spectra acquired in steady state temperature conditions, numerical simulations are performed to assess the quality of the reconstruction of dynamical temperature changes that are photothermally induced by illuminating the fiber with periodically intensitymodulated light. Comparison of the five neural networks investigated to multiple types of curve fits showed that using neural networks trained on a combination of the spectral characteristics improves the accuracy over use of a single independent input, with the greatest accuracy observed for inputs that included both intensity-based measurements (peak intensity) and shape-based measurements (normalized intensity at multiple wavelengths), with an ultimate accuracy of 0.29 K via numerical simulation based on experimental observations. The implications are that quantum dots can be used as a more stable and accurate fluorescence thermometer for solid materials and that use of neural networks for temperature reconstruction improves the accuracy of the measurement. Published by AIP Publishing.