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As the number of Internet of Things devices is rapidly increasing, there is an urgent need for sustainable and efficient energy sources and management practices in ambient environments. In response, we developed a high-efficiency ambient photovoltaic based on sustainable non-toxic materials and present a full implementation of a long short-term memory (LSTM) based energy management using on-device prediction on IoT sensors solely powered by ambient light harvesters. The power is supplied by dye-sensitised photovoltaic cells based on a copper(ii/i) electrolyte with an unprecedented power conversion efficiency at 38% and 1.0 V open-circuit voltage at 1000 lux (fluorescent lamp). The on-device LSTM predicts changing deployment environments and adapts the devices' computational load accordingly to perpetually operate the energy-harvesting circuit and avoid power losses or brownouts. Merging ambient light harvesting with artificial intelligence presents the possibility of developing fully autonomous, self-powered sensor devices that can be utilized across industries, health care, home environments, and smart cities.
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Dye-sensitized solar cells (DSCs) are celebrating their 30th birthday and they are attracting a wealth of research efforts aimed at unleashing their full potential. In recent years, DSCs and dye-sensitized photoelectrochemical cells (DSPECs) have experienced a renaissance as the best technology for several niche applications that take advantage of DSCs' unique combination of properties: at low cost, they are composed of non-toxic materials, are colorful, transparent, and very efficient in low light conditions. This review summarizes the advancements in the field over the last decade, encompassing all aspects of the DSC technology: theoretical studies, characterization techniques, materials, applications as solar cells and as drivers for the synthesis of solar fuels, and commercialization efforts from various companies.
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The impending implementation of billions of Internet of Things and wireless sensor network devices has the potential to be the next digital revolution, if energy consumption and sustainability constraints can be overcome. Ambient photovoltaics provide vast universal energy that can be used to realise near-perpetual intelligent IoT devices which can directly transform diffused light energy into computational inferences based on artificial neural networks and machine learning. At the same time, a new architecture and energy model needs to be developed for IoT devices to optimize their ability to sense, interact, and anticipate. We address the state-of-the-art materials for indoor photovoltaics, with a particular focus on dye-sensitized solar cells, and their effect on the architecture of next generation IoT devices and sensor networks.
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A new generation of octahedral iron(ii)-N-heterocyclic carbene (NHC) complexes, employing different tridentate C^N^C ligands, has been designed and synthesized as earth-abundant photosensitizers for dye sensitized solar cells (DSSCs) and related solar energy conversion applications. This work introduces a linearly aligned push-pull design principle that reaches from the ligand having nitrogen-based electron donors, over the Fe(ii) centre, to the ligand having an electron withdrawing carboxylic acid anchor group. A combination of spectroscopy, electrochemistry, and quantum chemical calculations demonstrate the improved molecular excited state properties in terms of a broader absorption spectrum compared to the reference complex, as well as directional charge-transfer displacement of the lowest excited state towards the semiconductor substrate in accordance with the push-pull design. Prototype DSSCs based on one of the new Fe NHC photosensitizers demonstrate a power conversion efficiency exceeding 1% already for a basic DSSC set-up using only the I-/I3 - redox mediator and standard operating conditions, outcompeting the corresponding DSSC based on the homoleptic reference complex. Transient photovoltage measurements confirmed that adding the co-sensitizer chenodeoxycholic acid helped in improving the efficiency by increasing the electron lifetime in TiO2. Time-resolved spectroscopy revealed spectral signatures for successful ultrafast (<100 fs) interfacial electron injection from the heteroleptic dyes to TiO2. However, an ultrafast recombination process results in undesirable fast charge recombination from TiO2 back to the oxidized dye, leaving only 5-10% of the initially excited dyes available to contribute to a current in the DSSC. On slower timescales, time-resolved spectroscopy also found that the recombination dynamics (longer than 40 µs) were significantly slower than the regeneration of the oxidized dye by the redox mediator (6-8 µs). Therefore it is the ultrafast recombination down to fs-timescales, between the oxidized dye and the injected electron, that remains as one of the main bottlenecks to be targeted for achieving further improved solar energy conversion efficiencies in future work.
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The field of photovoltaics gives the opportunity to make our buildings ''smart'' and our portable devices "independent", provided effective energy sources can be developed for use in ambient indoor conditions. To address this important issue, ambient light photovoltaic cells were developed to power autonomous Internet of Things (IoT) devices, capable of machine learning, allowing the on-device implementation of artificial intelligence. Through a novel co-sensitization strategy, we tailored dye-sensitized photovoltaic cells based on a copper(ii/i) electrolyte for the generation of power under ambient lighting with an unprecedented conversion efficiency (34%, 103 µW cm-2 at 1000 lux; 32.7%, 50 µW cm-2 at 500 lux and 31.4%, 19 µW cm-2 at 200 lux from a fluorescent lamp). A small array of DSCs with a joint active area of 16 cm2 was then used to power machine learning on wireless nodes. The collection of 0.947 mJ or 2.72 × 1015 photons is needed to compute one inference of a pre-trained artificial neural network for MNIST image classification in the employed set up. The inference accuracy of the network exceeded 90% for standard test images and 80% using camera-acquired printed MNIST-digits. Quantization of the neural network significantly reduced memory requirements with a less than 0.1% loss in accuracy compared to a full-precision network, making machine learning inferences on low-power microcontrollers possible. 152 J or 4.41 × 1020 photons required for training and verification of an artificial neural network were harvested with 64 cm2 photovoltaic area in less than 24 hours under 1000 lux illumination. Ambient light harvesters provide a new generation of self-powered and "smart" IoT devices powered through an energy source that is largely untapped.
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Due to ligand scrambling, the synthesis and investigation of the properties of heteroleptic Cu(I) complexes can be a challenging task. In this work, we have studied the optical and electrochemical properties of a series of homoleptic complexes, such as [Cu(dbda)2]+, [Cu(dmp)2]+, [Cu(Br-dmp)2]+, [Cu(bcp)2]+, [Cu(dsbtmp)2]+, [Cu(biq)2]+, and [Cu(dap)2]+ in solution, and those of their heteroleptics [Cu(dbda)(dmp)]+, [Cu(dbda)(Br-dmp)]+, [Cu(dbda)(bcp)]+, [Cu(dbda)(dsbtmp)]+, [Cu(dbda)(biq)]+, [Cu(dbda)(dap)]+ adsorbed on the surface of anatase TiO2 (dbda = 6,6'-dimethyl-2,2'-bipyridine-4,4'-dibenzoic acid; dmp = 2,9-dimethyl-1,10-phenanthroline; Br-dmp = 5-bromo-2,9-dimethyl-1,10-phenanthroline; bcp = bathocuproine or 2,9-dimethyl-4,7-diphenyl-1,10-phenanthroline; dsbtmp = 2,9-di(sec-butyl)-3,4,7,8-tetramethyl-1,10-phenanthroline; biq = 2,2'-biquinoline; dap = 2,9-dianisyl-1,10-phenanthroline). We show that the maximum absorption wavelengths of the heteroleptic complexes on TiO2 can be reasonably predicted from those of the homoleptic complexes in solution through a simple linear relation, whereas the prediction of their redox properties is less trivial. In the latter case, two different linear patterns emerge: one including the ligands bcp, biq, and dap and another one including the ligands dmp, Br-dmp, and dsbtmp. We offer an interpretation of the data based on the chemical structure of the ligands. On one hand, ligands bcp, biq, and dap possess a more extended π-conjugated system, which gives a more prominent contribution to the overall redox properties of the ligand dbda. On the other hand, the ligands dmp, Br-dmp, and dsbtmp are all phenanthroline-based containing alkyl substituents and contribute less than dbda to the overall redox properties.
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New low-cost photoactive hybrid materials based on organic luminescent molecules inserted into hydrotalcite (layered double hydroxides; LDH) were produced, which exploit the high-throughput liquid-assisted grinding (LAG) method. These materials are conceived for applications in dye-sensitized solar cells (DSSCs) as a co-absorbers and in silicon photovoltaic (PV) panels to improve their efficiency as they are able to emit where PV modules show the maximum efficiency. A molecule that shows a large Stokes' shift was designed, synthesized, and intercalated into LDH. Two dyes already used in DSSCs were also intercalated to produce two new nanocomposites. LDH intercalation allows the stability of organic dyes to be improved and their direct use in polymer melt blending. The prepared nanocomposites absorb sunlight from UV to visible and emit from blue to near-IR and thus can be exploited for light-energy management. Finally one nanocomposite was dispersed by melt blending into a poly(methyl methacrylate)-block-poly(n-butyl acrylate) copolymer to obtain a photoactive film.
