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The fabrication of varied molecular architectures in layer-by-layer (LbL) films is exploited to control the photoluminescence (PL) of poly(p-phenylene vinylene) (PPV) in an unprecedented way. This was achieved by controlling the Förster energy transfer between PPV layers (donors) and layers of a commercial azodye, Brilliant Yellow (BY) (acceptors). Energy transfer was controlled by inserting spacer layers of inert polymers between PPV and BY layers and by photoaligning the BY molecules via trans-cis-trans isomerization. The PPV/BY LbL films displayed polarized PL whose intensity could be varied almost continuously by changing the time of photoalignment, which was carried out by impinging a linearly polarized laser light simultaneously to the PL experiments. For PPV/BY films with no spacer layers, PL was completely quenched, but its intensity increased with the number of spacing layers. Further increase in PL was obtained by photoaligning the BY molecules perpendicularly to the PPV molecules. This minimizes energy transfer, since Förster processes are directional, dipole-dependent resonant transfers. Energy transfer is also controlled by imparting a preferential orientation of the PPV chains on PPV/BY LbL films deposited onto flexible Teflon substrates that may be stretched.

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The matrix effect is one of the challenges to be overcome for a successful analysis of soil samples using X-ray fluorescence (XRF) sensors. This work aimed at evaluation of a simple modeling approach consisted of Compton normalization (CN) and multivariate regressions (e.g., multiple linear regressions (MLR) and partial least squares regression (PLSR)) to overcome the soil matrix effect, and subsequently improve the prediction accuracy of key soil fertility attributes. A portable XRF was used for analyzing 102 soil samples collected from two agricultural fields with contrasting soil matrices. Using the intensity of emission lines as input, preprocessing methods included with and without the CN. Univariate regression models for the prediction of clay, cation exchange capacity (CEC), and exchangeable (ex-) K and Ca were compared with the corresponding MLR models to assess matrix effect mitigation. The MLR and PLSR models improved the prediction results of the univariate models for both preprocessing methods, proving to be promising strategies for mitigating the matrix effect. In turn, the CN also mitigated part of the matrix effect for ex-K, ex-Ca, and CEC predictions, by improving the predictive performance of these elements when used in univariate and multivariate models. The CN has not improved the prediction accuracy of clay. The prediction performances obtained using MLR and PLSR were comparable for all evaluated attributes. The combined use of CN with multivariate regressions (MLR or PLSR) achieved excellent prediction results for CEC (R2 = 0.87), ex-K (R2 ≥ 0.94), and ex-Ca (R2 ≥ 0.96), whereas clay predictions were comparable with and without CN (0.89 ≤ R2 ≤ 0.92). We suggest using multivariate regressions (MLR or PLSR) combined with the CN to remove the soil matrix effects and consequently result in optimal prediction results of the studied key soil fertility attributes. The prediction performance observed for this solution showed comparable results to the approach based on the preprogrammed measurement package tested (Geo Exploration package, Bruker AXS, Madison, WI, USA).