Perovskite solar cells (PSCs) have already achieved comparable performance to industrially established silicon technologies. However, high performance and stability must be also be achieved at large area and low cost to be truly commercially viable. The fully printable triple-mesoscopic carbon perovskite solar cell (mCPSC) has demonstrated unprecedented stability and can be produced at low capital cost with inexpensive materials. These devices are inherently scalable, and large-area modules have already been fabricated using low-cost screen printing. As a uniquely stable, scalable and low-cost architecture, mCPSC research has advanced significantly in recent years. This review provides a detailed overview of advancements in the materials and processing of each individual stack layer as well as in-depth coverage of work on perovskite formulations, with the view of highlighting potential areas for future research. Long term stability studies will also be discussed, to emphasise the impressive achievements of mCPSCs for both indoor and outdoor applications.

The number of interconnected devices, often referred to as the Internet of Things (IoT), is increasing at a considerable rate. It is inevitable therefore that so too will the energy demand. IoT describes a range of technologies such as sensors, software, smart meters, wearable devices, and communication beacons for the purpose of connecting and exchanging data with other devices and systems over the internet. Often not located near a mains supply power source, these devices may be reliant on primary battery cells. To avoid the need to periodically replace these batteries, it makes sense to integrate the technologies with a photovoltaic (PV) cell to harvest ambient light, so that the technologies can be said to be self-powered. Perovskite solar cells have proven extremely efficient in low-light conditions but in the absence of ambient and low-light testing standards, or even a consensus on what is defined by “ambient light”, it is difficult to estimate the energy yield of a given PV technology in a given scenario. Ambient light harvesting is complex, subject to spectral considerations, and whether the light source is directly incident on the PV cell. Here, we present a realistic scenario-driven method for measuring the energy yield for a given PV technology in various situations in which an IoT device may be found. Furthermore, we show that laboratory-built p-i-n perovskite devices, for many scenarios, produce energy yields close to that of commercial GaAs solar cells. Finally, we demonstrate an IoT device, powered by a mesoporous carbon perovskite solar module and supercapacitor, and operating through several day–night cycles.

Fully printable carbon-based perovskite solar cells (C-PSCs) represent some of the most promising perovskite solar cell (PSC) architectures. Highly scalable, stable, and low in cost—these devices consist of a TiO₂ compact layer (C-TiO₂) and three sequentially screen-printed mesoporous layers of TiO₂, ZrO₂, and carbon, through which perovskite is infiltrated. While there has been remarkable progress in optimizing and scaling up deposition of mesoporous layers and perovskite, few publications have focused on optimizing C-TiO₂. In this work, we investigate the potential for substituting commonly used spray pyrolysis with more easily scaled screen-printing. It was found that when comparing layers of similar thickness, 1 cm² devices fabricated with printed C-TiO₂ exhibited similar power conversion efficiency (PCE) to those fabricated with spray pyrolysis. In contrast, thicker-printed C-TiO₂ led to lower efficiency. The influence of TiCl₄ treatment on the quality of produced compact layers was also examined. This proved beneficial, mostly in the printed films, where a champion PCE of 13.11% was attained using screen-printed, TiCl₄ treated C-TiO₂. This work proves that screen-printing is a viable replacement for spray pyrolysis in C-PSCs fabrication.

In the current study, a novel approach combining quantile regression with nonlinear mixed-effects (QR-NLME) modeling was applied to predict the maximum crown width (cw_max) of three economically important forest species—the European beech (Fagus sylvatica L.), the black pine (Pinus nigra Arn.), and the Calabrian pine (Pinus brutia Ten.) at tree level. A power QR-NLME model was fitted first to a dataset including 1414 European beech trees obtained from 29 randomly distributed sample plots, 770 black pine trees from 25 sample plots, and 1880 Calabrian pine trees from 41 sample plots in Greece, to predict the cw_max at tree level. Additionally, a nonlinear mixed-effects model (NLME) was fitted to the same dataset to predict the average crown width at tree level for all species. In the second stage, the crown competition factor (CCF) was estimated based on the population average response of the cw_max predictions. The proposed approach presented sound results when compared with the outcomes of relevant models from other regions fitted to open-grown tree data, and therefore, it can be well implemented on clustered data structures, in cases of absence of open-grown tree data.