Noel Bristow

Crop productivity is highly dependent on the availability of soluble nitrogen (N), e.g. nitrate, in soil. When N levels are low, fertilisers are applied to replenish the soil’s reserves. Typically the timing of these applications is based on paper-based guidance and sensor-based measurements of canopy greenness, which provides an indirect measure of soil N status. However this approach often means that N fertiliser is applied inappropriately or too late, resulting in excess N being lost to the environment, or too little N to meet crop demand. To promote greater N use efficiency and improve agricultural sustainability, we developed an Internet of Things (IoT) approach for the real-time measurement of soil nitrate levels using ion-selective membrane sensors in combination with digital soil moisture probes. The node incorporates state-of-the-art IoT connectivity using a LoRaWAN transceiver. The sensing platform can transfer real-time data via a cloud-connected gateway for processing and storage. In summary, we present a validated soil sensor system for real-time monitoring of soil nitrate concentrations, which can support fertiliser management decisions, improve N use efficiency and reduce N losses to the environment.

Passive acoustic monitoring (PAM) is a common approach to monitor marine mammal populations, for species of dolphins, porpoises and whales that use sound for navigation, feeding and communication. PAM produces large datasets which benefit from the application of machine learning algorithms to automatically detect and classify the vocalisations of these animals. We present a deep learning approach for the classification of dolphins’ echolocation clicks into two species groups in an environment with high background noise. We compare the use of Convolutional Neural Networks (CNN) and Recurrent Neural Network (RNN), in which we feed the models the raw waveform data and spectrograms. We show that both models perform well, with the highest performance achieved by a CNN fed with spectrograms (F1 score 97 %) and an RNN fed with raw data (F1 score 96%) fitted with Gated Recurrent Units (GRU). We recommend the use of such models to classify echolocation clicks in marine environments where background noise levels exhibit high spatial and temporal variance. In particular, the RNN showed excellent performance, while being fed with raw data, in terms of reduced processing time and storage. Deep learning automatically extracts effective features from the raw waveform in the training process through multiple layers of the model, without the need to rely on feature extraction in a separate pre-processing step.

The use of solar panels for the purpose of converting solar energy into electrical energy has been increasingly common in the community, where the vast majority of these panels are produced from inorganic materials, especially silicon (Si). In recent decades, many researchers have been analyzing organic and hybrid materials in order to apply them to photovoltaic devices. The use of organic and hybrid materials in devices is advantageous due to some factors, such as: low production cost, variety of materials available, production of flexible devices. A fundamental analysis for any photovoltaic device is the external quantum efficiency measure (EQE). This technique correlates the number of incident photons with the number of electrons generated, making it possible to know in which region of the electromagnetic spectrum the photovoltaic device is more efficient. This work aims to apply the external quantum efficiency characterization technique for the evaluation of different types of photovoltaic devices in terms of their stability

Luminescence down-shifting (LDS) is used in several photovoltaic technologies aiming to improve the photon conversion efficiency (PCE) of the devices through the increase of the light harvesting in the regions of the electromagnetic spectrum where the EQE of the solar cells is poor. The aim of this work was to produce films of mixtures (blends) of two luminescent materials, dispersed in a poly-methyl methacrylate (PMMA) matrix, hoping to improve their properties both as LDS layer and as UV filter when applied on the clear, external surface of P3HT:PC ₆₁ BM photovoltaic devices. The best results led to an increment of 7.4% in the PCE of the devices, and a six fold enhancement in their half-life ( T _50% ). This study indicates that multidye LDS layers with optimized optical properties can lead to an effective improvement in the performance and operational stability of OPVs.

The rooftops of Cardiff City Hall were surveyed to establish potential areas for commercial-scale photovoltaic (PV) system design. The orientation and tilt angles of suitable unshaded roof areas were measured for accurate PV system simulation. The performance of two PV technologies, polycrystalline silicon (p-Si) and heterojunction with intrinsic thin layers (HIT) was investigated. From the analysis of simulation, experimental, environmental and economic data, HIT was found to be the best-performing PV technology for system installation. Superior performance of HIT under diffuse sunlight conditions, typical of the UK climate, was demonstrated. Additionally, the maximum power temperature coefficient, verified during experimental work, was lower than the p-Si alternative (−0·28 against −0·50%/°C). Electricity demand data for City Hall were analysed and 8·1% of the annual electricity demand (solar fraction) could be supplied by an 88 kWp HIT PV system. The HIT PV system modelled would significantly improve the energy performance of Cardiff City Hall, avoiding >40 000 kg carbon dioxide emissions annually. The levelised cost of energy from one array (B, £0·11/kWh) was less than the current day tariff rate for grid import (£0·1173). The economic and environmental benefits of well-designed high-efficiency PV systems in the UK at commercial scale are also demonstrated.