Environmental plastics in the context of UV radiation, climate change, and the Montreal Protocol.

There are close links between solar UV radiation, climate change, and plastic pollution. UV-driven weathering is a key process leading to the degradation of plastics in the environment but also the formation of potentially harmful plastic fragments such as micro- and nanoplastic particles. Estimates of the environmental persistence of plastic pollution, and the formation of fragments, will need to take in account plastic dispersal around the globe, as well as projected UV radiation levels and climate change factors.

Plastics in the environment in the context of UV radiation, climate change and the Montreal Protocol: UNEP Environmental Effects Assessment Panel, Update 2023.

This Assessment Update by the Environmental Effects Assessment Panel (EEAP) of the United Nations Environment Programme (UNEP) considers the interactive effects of solar UV radiation, global warming, and other weathering factors on plastics. The Assessment illustrates the significance of solar UV radiation in decreasing the durability of plastic materials, degradation of plastic debris, formation of micro- and nanoplastic particles and accompanying leaching of potential toxic compounds. Micro- and nanoplastics have been found in all ecosystems, the atmosphere, and in humans. While the potential biological risks are not yet well-established, the widespread and increasing occurrence of plastic pollution is reason for continuing research and monitoring. Plastic debris persists after its intended life in soils, water bodies and the atmosphere as well as in living organisms. To counteract accumulation of plastics in the environment, the lifetime of novel plastics or plastic alternatives should better match the functional life of products, with eventual breakdown releasing harmless substances to the environment.

Using spatio-temporal graph neural networks to estimate fleet-wide photovoltaic performance degradation patterns.

Accurate estimation of photovoltaic (PV) system performance is crucial for determining its feasibility as a power generation technology and financial asset. PV-based energy solutions offer a viable alternative to traditional energy resources due to their superior Levelized Cost of Energy (LCOE). A significant challenge in assessing the LCOE of PV systems lies in understanding the Performance Loss Rate (PLR) for large fleets of PV systems. Estimating the PLR of PV systems becomes increasingly important in the rapidly growing PV industry. Precise PLR estimation benefits PV users by providing real-time monitoring of PV module performance, while explainable PLR estimation assists PV manufacturers in studying and enhancing the performance of their products. However, traditional PLR estimation methods based on statistical models have notable drawbacks. Firstly, they require user knowledge and decision-making. Secondly, they fail to leverage spatial coherence for fleet-level analysis. Additionally, these methods inherently assume the linearity of degradation, which is not representative of real world degradation. To overcome these challenges, we propose a novel graph deep learning-based decomposition method called the Spatio-Temporal Graph Neural Network for fleet-level PLR estimation (PV-stGNN-PLR). PV-stGNN-PLR decomposes the power timeseries data into aging and fluctuation components, utilizing the aging component to estimate PLR. PV-stGNN-PLR exploits spatial and temporal coherence to derive PLR estimation for all systems in a fleet and imposes flatness and smoothness regularization in loss function to ensure the successful disentanglement between aging and fluctuation. We have evaluated PV-stGNN-PLR on three simulated PV datasets consisting of 100 inverters from 5 sites. Experimental results show that PV-stGNN-PLR obtains a reduction of 33.9% and 35.1% on average in Mean Absolute Percent Error (MAPE) and Euclidean Distance (ED) in PLR degradation pattern estimation compared to the state-of-the-art PLR estimation methods.

Degradation Mechanism Detection in Photovoltaic Backsheets by Fully Convolutional Neural Network.

Materials and devices age with time. Material aging and degradation has important implications for lifetime performance of materials and systems. While consensus exists that materials should be studied and designed for degradation, materials inspection during operation is typically performed manually by technicians. The manual inspection makes studies prone to errors and uncertainties due to human subjectivity. In this work, we focus on automating the process of degradation mechanism detection through the use of a fully convolutional deep neural network architecture (F-CNN). We demonstrate that F-CNN architecture allows for automated inspection of cracks in polymer backsheets from photovoltaic (PV) modules. The developed F-CNN architecture enabled an end-to-end semantic inspection of the PV module backsheets by applying a contracting path of convolutional blocks (encoders) followed by an expansive path of decoding blocks (decoders). First, the hierarchy of contextual features is learned from the input images by encoders. Next, these features are reconstructed to the pixel-level prediction of the input by decoders. The structure of the encoder and the decoder networks are thoroughly investigated for the multi-class pixel-level degradation type prediction for PV module backsheets. The developed F-CNN framework is validated by reporting degradation type prediction accuracy for the pixel level prediction at the level of 92.8%.

Temporal evolution and pathway models of poly(ethylene-terephthalate) degradation under multi-factor accelerated weathering exposures.

Photolytic and hydrolytic degradation of poly(ethylene-terephthalate) (PET) polymers with different stabilizers were performed under multiple accelerated weathering exposures and changes in the polymers were monitored by various evaluation techniques. Yellowing was caused by photolytic degradation and haze formation was induced by combined effects of photolytic and hydrolytic degradation. The formation of light absorbing chromophores and bleaching of the UV stabilizer additive were recorded through optical spectroscopy. Chain scission and crystallization were found to be common mechanisms under both photolytic and hydrolytic conditions, based on the infrared absorption of the carbonyl (C = O) band and the trans ethylene glycol unit, respectively. The degradation mechanisms determined from these evaluations were then used to construct a set of degradation pathway network models using the network structural equation modeling (netSEM) approach. This method captured the temporal evolution of degradation by assessing statistically significant relationships between applied stressors, mechanistic variables, and performance level responses. Quantitative pathway equations provided the contributions from mechanistic variables to the response changes.

Multivariate multiple regression models of poly(ethylene-terephthalate) film degradation under outdoor and multi-stressor accelerated weathering exposures.

Developing materials for use in photovoltaic (PV) systems requires knowledge of their performance over the warranted lifetime of the PV system. Poly(ethylene-terephthalate) (PET) is a critical component of PV module backsheets due to its dielectric properties and low cost. However, PET is susceptible to environmental stressors and degrades over time. Changes in the physical properties of nine PET grades were modeled after outdoor and accelerated weathering exposures to characterize the degradation process of PET and assess the influence of stabilizing additives and weathering factors. Multivariate multiple regression (MMR) models were developed to quantify changes in color, gloss, and haze of the materials. Natural splines were used to capture the non-linear relationship between predictors and responses. Model performance was evaluated via adjusted-R2 and root mean squared error values from leave-one-out cross validation analysis. All models described over 85% of the variation in the data with low relative error. Model coefficients were used to assess the influence of weathering stressors and material additives on the property changes of films. Photodose was found to be the primary degradation stressor and moisture was found to increase the degradation rate of PET. Direct moisture contact was found to impose more stress on the material than airbone moisture (humidity). Increasing the concentration of TiO2 was found to generally decrease the degradation rate of PET and mitigate hydrolytic degradation. MMR models were compared to physics-based models and agreement was found between the two modeling approaches. Cross-correlation of accelerated exposures to outdoor exposures was achieved via determination of cross-correlation scale factors. Cross-correlation revealed that direct moisture contact is a key factor for reliable accelerated weathering testing and provided a quantitative method to determine when accelerated exposure results can be made more aggressive to better approximate outdoor exposure conditions.

Predictive models of poly(ethylene-terephthalate) film degradation under multi-factor accelerated weathering exposures.

Accelerated weathering exposures were performed on poly(ethylene-terephthalate) (PET) films. Longitudinal multi-level predictive models as a function of PET grades and exposure types were developed for the change in yellowness index (YI) and haze (%). Exposures with similar change in YI were modeled using a linear fixed-effects modeling approach. Due to the complex nature of haze formation, measurement uncertainty, and the differences in the samples' responses, the change in haze (%) depended on individual samples' responses and a linear mixed-effects modeling approach was used. When compared to fixed-effects models, the addition of random effects in the haze formation models significantly increased the variance explained. For both modeling approaches, diagnostic plots confirmed independence and homogeneity with normally distributed residual errors. Predictive R2 values for true prediction error and predictive power of the models demonstrated that the models were not subject to over-fitting. These models enable prediction under pre-defined exposure conditions for a given exposure time (or photo-dosage in case of UV light exposure). PET degradation under cyclic exposures combining UV light and condensing humidity is caused by photolytic and hydrolytic mechanisms causing yellowing and haze formation. Quantitative knowledge of these degradation pathways enable cross-correlation of these lab-based exposures with real-world conditions for service life prediction.

Taxonomic classification of phytoplankton with multivariate optical computing, part I: design and theoretical performance of multivariate optical elements.

Phytoplankton are single-celled, photosynthetic algae and cyanobacteria found in all aquatic environments. Differential pigmentation between phytoplankton taxa allows use of fluorescence excitation spectroscopy for discrimination and classification. For this work, we applied multivariate optical computing (MOC) to emulate linear discriminant vectors of phytoplankton fluorescence excitation spectra by using a simple filter-fluorometer arrangement. We grew nutrient-replete cultures of three differently pigmented species: the coccolithophore Emiliania huxleyi, the diatom Thalassiosira pseudonana, and the cyanobacterium Synechococcus sp. Linear discriminant analysis (LDA) was used to determine a suitable set of linear discriminant functions for classification of these species over an optimal wavelength range. Multivariate optical elements (MOEs) were then designed to predict the linear discriminant scores for the same calibration spectra. The theoretical performance specifications of these MOEs are described.

Taxonomic classification of phytoplankton with multivariate optical computing, part II: design and experimental protocol of a shipboard fluorescence imaging photometer.

Differential pigmentation between phytoplankton allows use of fluorescence excitation spectroscopy for the discrimination and classification of different taxa. Here, we describe the design and performance of a fluorescence imaging photometer that exploits taxonomic differences for discrimination and classification. The fluorescence imaging photometer works by illuminating individual phytoplankton cells through an asynchronous spinning filter wheel, which produces bar code-like streaks in a fluorescence image. A filter position is covered with an opaque filter to create a reference dark position in the filter wheel rotation that is used to match each fluorescence streak with the corresponding filter. Fluorescence intensities of the imaged streaks are then analyzed for the purpose of spectral analysis, which allows taxonomic classification of the organism that produced the streaks. The theoretical performance and signal-to-noise ratio (SNR) specifications of these MOEs are described in Part I of this series. This report describes optical layout, flow cell design, magnification, depth of field, constraints on filter wheel and flow velocities, procedures for blank subtraction and flat-field correction, the measurement scheme of the instrument, and measurement of SNR as a measurement of filter wheel frequency. This is followed by an analysis of the sources of variance in measurements made by the photometer on the coccolithophore Emiliania huxleyi. We conclude that the SNR of E. huxleyi measurements is not limited by the sensitivity or noise attributes of the measurement system, but by dynamics in the fluorescence efficiency of the E. huxleyi cells. Even so, the minimum SNR requirements given in Part I for the instrument are met.

Taxonomic classification of phytoplankton with multivariate optical computing, part III: demonstration.

We describe the automatic analysis of fluorescence tracks of phytoplankton recorded with a fluorescence imaging photometer. The optical components and construction of the photometer were described in Part I and Part II of this series in this issue. An algorithm first isolates tracks corresponding to a single phytoplankter transit in the nominal focal plane of a flow cell. Then, the fluorescence streaks in the track that correspond to individual optical elements on the filter wheel are identified. The fluorescence intensity of each streak is integrated and used to calculate ratios. This approach was tested using 853 fluorescence measurements of the coccolithophore Emiliania huxleyi and the diatom Thalassiosira pseudonana. Average intensity ratios for the two classes closely follow those predicted in Part I of this series, with a distribution of ratios in each class that is consistent with the signal-to-noise ratio calculations in Part II for single cells. No overlap of the two class ratios was observed, yielding perfect classification.