Perovskite materials with ABX3 chemistries are promising candidates for photovoltaic applications, owing to their suitable optoelectronic properties. However, they are highly hydrophilic and unstable in nature, limiting the commercialization of perovskite photovoltaics. Mixed halide ion-doped perovskites are reported to be more stable compared to simple ABX3 chemistries. This paper describes ab initio modeling, synthesis, and characterization of thiocyanate doped lead iodide CH3NH3PbI(3−x)(SCN)x perovskites. Several perovskite chemistries with an increasing concentration of (SCN)− at x = 0, 0.25, 0.49, 1.0, 1.45 were evaluated. Subsequently, ‘n-i-p’ and ‘p-i-n’ perovskite solar device architectures, corresponding to x = 0, 0.25, 0.49, 1.0 thiocyanate doped lead halide perovskite chemistry were fabricated. The study shows that among all the devices fabricated for different compositions of perovskites, p-i-n perovskite solar cell fabricated using CH3NH3PbI(3−x)(SCN)x perovskite at x = 1.0 exhibited the highest stability and device efficiency was retained until 450 h. Finally, a solar panel was fabricated and its stability was monitored.
This paper presents prediction of shelf-life of ‘Kesar’ cultivar of mangoes stored under specified conditions based on their respiration rate and ripeness levels. A deep-CNN was fine-tuned on 1524 image data of mangoes stored under different conditions to classify the ripeness levels of mangoes as ‘unripe’, ‘early-ripe’, ‘partially-ripe’ and ‘ideally-ripe’. CO2 respiration rate (RRCO2) was further calculated using principle of enzyme kinetics to establish a correlation between RRCO2 and ripeness levels. A Support Vector Regression model was employed to predict the shelf life and ripeness levels of mangoes under different storage conditions, thereby creating an AI based soft-sensor. The developed methodology can be used for other climacteric fruits besides mangoes. This solution can be used by producers and distributors for post-harvest handling of climacteric fruits like mango. It will also aid retailers in taking dynamic decisions with respect to pricing, logistics and storage conditions to be maintained to get the desired ripening rate, thus, contributing to reduction of wastage of fruits and subsequent economic losses.Article highlights Variation in CO2 respiration rate of ‘Kesar’ mangoes over different maturity stages were observed under different supply chain scenarios simulated in lab environment AI models were developed based on respiration rate and ripeness levels for prediction of shelf life of mangoes under different supply chain scenarios. These models once deployed helps all stake holders in fruit supply chain to take dynamic decisions such as repricing, recycling and repurposing based on the predicted shelf life thus minimizing wastage and maximizing profit.
Ideally, ripe fruits offer appropriate nutritional content and best quality in terms of taste and flavour. Prediction of ideally ripe climacteric fruits acts as the main marketing indicator for quality from the consumer perspective and thus renders it a genuine industrial concern for all the stakeholders of the fruit supply chain. However, the building of fruit-specific individual model for the prediction of ripeness level remains an existing challenge due to the scarcity of sufficient labeled experimental data for each fruit. This paper describes the development of generic AI models based on the similarity in physico-chemical degradation phenomena of climacteric fruits for prediction of ‘unripe’ and ‘ideally ripe’ levels using ‘zero-shot’ transfer learning techniques. Experiments were performed on a variety of climacteric and non-climacteric fruits and it was observed that transfer learning works better for fruits within a cluster (climacteric fruits) as compared to across clusters (climacteric to non-climacteric fruits). The main contributions of this work are two-fold (i) Using domain knowledge of food chemistry to label the data in terms of age of the fruit (ii) We hypothesize and prove that the zero-shot transfer learning works better within a set of fruits, sharing similar degradation chemistry depicted by their visual properties like black spot formations, wrinkles, discoloration, etc. To the best of our knowledge, this is the first study to demonstrate the same.
Ideally, ripe fruits offer appropriate nutritional content and best quality in terms of taste and flavour. Prediction of ripe climacteric fruits acts as the main marketing indicator for quality from the consumer perspective and thus renders it a genuine industrial concern for all the stakeholders of the fruit supply chain. However, the building of fruit-specific individual model for the prediction of ripeness level remains an existing challenge due to the scarcity of sufficient labeled experimental data for each fruit. This paper describes the development of generic AI models based on the similarity in physico-chemical degradation phenomena of climacteric fruits for prediction of ‘unripe’ and ‘ripe’ levels using ‘zero-shot’ transfer learning techniques. Experiments were performed on a variety of climacteric and non-climacteric fruits, and it was observed that transfer learning works better for fruits within a cluster (climacteric fruits) as compared to across clusters (climacteric to non-climacteric fruits). The main contributions of this work are two-fold (i) Using domain knowledge of food chemistry to label the data in terms of age of the fruit, (ii) We hypothesize and prove that the zero-shot transfer learning works better within a set of fruits, sharing similar degradation chemistry depicted by their visual properties like black spot formations, wrinkles, discoloration, etc. The best models trained on banana, papaya and mango dataset resulted in s zero-shot transfer learned accuracies in the range of 70 to 82 for unknown climacteric fruits. To the best of our knowledge, this is the first study to demonstrate the same.
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