Copper Glufosinate-Based Metal−Organic Framework as a Novel Multifunctional Agrochemical

Pesticides are agrochemical compounds used to kill pests (insects, rodents, fungi, or unwanted plants), which are key to meet the world food demand. Regrettably, some important issues associated with their widespread/extensive use (contamination, bioaccumulation, and development of pest resistances) demand a reduction in the amount of pesticide applied in crop protection. Among the novel technologies used to combat the deterioration of our environment, metal−organic frameworks (MOFs) have emerged as innovative and promising materials in agroindustry since they possess several features (high porosity, functionalizable cavities, ecofriendly composition, etc.) that make them excellent candidates for the controlled release of pesticides. Moving toward a sustainable development, in this work, we originally describe the use of pesticides as building blocks for the MOF construction, leading to a new type of agricultural applied MOFs (or AgroMOFs). Particularly, we have prepared a novel 2D-MOF (namely, GR-MOF-7) based on the herbicide glufosinate and the widely used antibacterial and fungicide Cu2+. GR-MOF-7 crystallizes attaining a monoclinic P21/c space group, and the asymmetric unit is composed of one independent Cu2+ ion and one molecule of the Glu2− ligand. Considering the significant antibacterial activity of Cu-based compounds in agriculture, the potential combined bactericidal and herbicidal effect of GR-MOF-7 was investigated. GR-MOF-7 shows an important antibacterial activity against Staphylococcus aureus and Escherichia coli (involved in agricultural animal infections), improving the results obtained with its individual or even physical mixed precursors [glufosinate and Cu(NO3)2]. It is also an effective pesticide against germination and plant growth of the weed Raphanus sativus, an invasive species in berries and vines crops, demonstrating that the construction of MOFs based on herbicide and antibacterial/antifungal units is a promising strategy to achieve multifunctional agrochemicals. To the best of our knowledge, this first report on the synthesis of an MOF based on agrochemicals (what we have named AgroMOF) opens new ways on the safe and efficient MOF application in agriculture.project MOFseidon PID2019-104228RB-I00Juan de la Cierva incorporation JC2019-038894-I and Multifunctional Metallodrugs in Diagnosis and Therapy Network (MICIU) RED2018-102471-TComunidad de MadridEuropean Regional Development Fund-FEDER 2014-2020-OE REACTUEUniversidad de Granada/CBUAEuropean Union NextGenerationEU/PRT

Automatic wavelength spectrometer calibration during arc-welding process

Spectroscopic analysis techniques are widely used in a variety of scientific areas. The availability of low-cost CCD spectrometers has also allowed their development in several industrial applications, like on-line analysis of arc and laser welding processes. A correct spectrometer wavelength calibration is always required, specially when changes in ambient temperature are to be found, or when the optical fiber attached to the spectrometer is replaced. This calibration procedure commonly involves the recalculation of a pixelwavelength polynomial by means of regression techniques after having defined a new experimental setup. Besides, specific calibration lamps are needed to use some known emission lines in the regression stage. In this paper, a technique which allows a real-time, in-process automatic wavelength calibration of CCD spectrometers in arc-welding processes is presented. The key point in the automatic calibration process is the real-time identification of some particular emission lines emitted from the plasma generated during the welding process. TIG welding tests performed on stainless steel plates will show the feasibility of the proposed technique. As well as for laser welding, the automatic wavelength calibration procedure could be easily extended to some other spectroscopic techniques

Hyperspectral imaging for diagnosis and quality control in agri-food and industrial sectors

Optical spectroscopy has been utilized in various fields of science, industry and medicine, since each substance is discernible from all others by its spectral properties. However, optical spectroscopy traditionally generates information on the bulk properties of the whole sample, and mainly in the agri-food industry some product properties result from the heterogeneity in its composition. This monitoring is considerably more challenging and can be successfully achieved by the so-called hyperspectral imaging technology, which allows the simultaneous determination of the optical spectrum and the spatial location of an object in a surface. In addition, it is a nonintrusive and non-contact technique which gives rise to a great potential for industrial applications and it does not require any particular preparation of the samples, which is a primary concern in food monitoring. This work illustrates an overview of approaches based on this technology to address different problems in agri-food and industrial sectors. The hyperspectral system was originally designed and tested for raw material on-line discrimination, which is a key factor in the input stages of many industrial sectors. The combination of the acquisition of the spectral information across transversal lines while materials are being transported on a conveyor belt, and appropriate image analyses have been successfully validated in the tobacco industry. Lastly, the use of imaging spectroscopy applied to online welding quality monitoring is discussed and compared with traditional spectroscopic approaches in this regard

Use of the plasma RMS signal for on-line welding quality monitoring

In this paper a new spectroscopic monitoring parameter is proposed for the on-line monitoring of welding processes, the plasma RMS signal, which is determined by considering the contribution from the spectral samples over a particular spectral window. This parameter is directly related to the heat input that can be estimated by measuring both welding voltage and current, but it exhibits a higher sensitivity to the appearance of weld defects. A comparison between the results obtained from the different spectroscopic parameters will be presented, with data from both experimental and field arc-welding tests

Hyperspectral imaging sustains production-process competitiveness

A newly developed imaging system aids companies in the agri-food and industrial sectors to achieve high-speed online inspection and enhanced quality control

Welding diagnostics by means of particle swarm optimization and feature selection

In a previous contribution, a welding diagnostics approach based on plasma optical spectroscopy was presented. It consisted of the employment of optimization algorithms and synthetic spectra to obtain the participation profiles of the species participating in the plasma. A modification of the model is discussed here: on the one hand the controlled random search algorithm has been substituted by a particle swarm optimization implementation. On the other hand a feature selection stage has been included to determine those spectral windows where the optimization process will take place. Both experimental and field tests will be shown to illustrate the performance of the solution that improves the results of the previous work.This work has been supported by the TEC2010-20224-C02-02 and OPENAER CENIT 2007–2010 projects

Welding diagnostics based on feature selection and optimization algorithms

In a previous paper a new approach was explored where the output parameters of a welding monitoring system based on plasma spectroscopy were the participation profiles of plasma ions and neutral atoms. They were obtained by the generation of synthetic spectra and the use of an optimization algorithm, showing correlation to the appearance of defects on the seams. In this work a feature selection algorithm is included in the model to determine the most discriminant wavelengths in terms of defect detection, thus allowing to reduce the spectral range where the synthetic spectra are generated. This should also give rise to an improvement in the overall computational performance of the algorithm. Alternatives to the use of controlled randomn search algorithms will be also explored, and the resulting model will be checked by means of experimental and field tests of arc-welding processes