Metal halide perovskites are a promising photovoltaic technology for energy harvesting due to their potential for low cost via high-speed manufacturing and their flexible light form factors offering high power per weight. This study presents an investigation of the energy harvesting performance of perovskite solar cells under monochromatic illumination via finite element simulations and experimental validation with high-efficiency double cation perovskite solar cells. Device performance across a broad range of illumination intensity is analyzed, providing insights into the mechanisms limiting energy harvesting in medium- and long-range wireless power transfer. The simulations also provide a guideline for compositional engineering of wide bandgap perovskites to improve the spectral match to efficient monochromatic sources. Based on these results, we show how perovskite solar cells can become a platform for efficient (>33%) medium-range wireless power transfer at the 5–50 m scale for power levels of 1 mW to 1 W.

Metal halide perovskites have emerged as a promising class of materials for photovoltaic energy harvesting to power wireless electronics due to their potential for being produced at low cost via high speed manufacturing1,2 as well as their flexible form factors offering high power per weight.3 These capabilities make perovskite solar cells (PSCs) ideal candidates for enabling a new breed of sustainable, battery-free large area electronics4 with sensing,5 processing, and communication6 capabilities for applications in systems such as lightweight wearable biosensors.7 Previous studies of energy harvesting by perovskite solar cells have focused on performance under undirected ambient low-intensity fluorescent lighting, showing an efficiency of up to 36% for multiple anion absorbers tuned to increase the bandgap to ∼1.8 eV8 and an efficiency above 43% for FAPbBr3 absorbers with a bandgap above 2.1 eV.9 For harvesting indoor lighting (e.g., 1000 lx), for example, perovskite cells have been shown to outperform GaAs, dye-sensitized solar cells (DSCs), and organic photovoltaics (OPV).10 The dual functions of perovskites as light emitters and absorbers can also be leveraged for all-perovskite optical wireless power transfer.11 These past studies focus on the performance of perovskite cells at low levels of incident intensity in the range of <1 mW/cm2 typical of ambient indoor lighting.5 At low levels of illumination, however, the resulting power harvested by a perovskite cell remains at the ∼100 µW/cm2 level—insufficient to satisfy the requirements of many internet of things (IoT) devices that demand 10–100 mW for wireless communication (e.g., Bluetooth low energy12,13). Although intermittent sensing and communication combined with energy storage can extend the capabilities of these low-power systems,5 multifunctional systems with enhanced computing and sensing functionality will demand higher power levels than can be achieved with ambient light.

To deliver higher power for enabling battery-free IoT wireless devices, one promising strategy that has been demonstrated is to use high-efficiency solar cells with directed illumination as a mode for efficient photonic wireless power transmission (WPT). This method has been applied for high-power wireless charging at greater than 10 W14 as well as in applications such as in vivo energy harvesting devices.15 A key advantage of this photonic WPT method is that it provides the ability to transmit power and data simultaneously via a single optical link.16 Another significant benefit is the self-charging capability that can eliminate the need to replace batteries in networks of miniaturized mobile devices. However, past demonstrations of photonic WPT suffered from poor efficiency resulting from the fundamental mismatch between the low bandgap (1.12 eV) of standard Si solar cells and the energies of emitted photons from high-efficiency lasers and light emitting diodes (LEDs) (e.g., 2.3 eV for green sources). III–V cells can offer higher bandgaps that more efficiently convert transmitted optical power, but their high cost deriving from the need for epitaxial deposition17 presents a barrier to integrating WPT with lower cost flexible and printed electronic devices suitable for IoT applications.18 These factors motivate the use of metal halide perovskites as a low-cost, high-efficiency technology. Perovskite solar cells could be the solution to making photonic WPT more economical and more efficient via their printability and their broadly tunable bandgaps for matching LED emission wavelengths.19 This has been recently shown in the work by Guo et al.,9 which performed underwater laser energy transfer at high efficiency (>43%) using wide bandgap FAPbBr3 perovskite solar cells designed with optimal charge transport layer architectures. Building off this prior work demonstrating the potential of perovskite-based wireless power transfer, the present study maps the efficiency of perovskite solar cells of multiple bandgaps operating under variable intensity monochromatic illumination via technology computer aided design (TCAD) simulations and experimentally validates these results with high-efficiency double cation perovskite solar cells. Based on these results, we show that perovskite solar cells can become a platform for high-efficiency (>33%) medium-range wireless power transfer through air at the 5–50 m scale for power levels of 1 mW to 1 W.

Photonic WPT can be achieved by combining a high-efficiency light source with a receiver comprised of a single solar cell or an integrated solar module. Applications of photonic WPT spanning a range from ultra-low-power systems, such as solar-powered radio frequency identification (RFID)20 tags, to high-intensity pulsed charging call for understanding the behavior of the solar cell receiver under these varying illumination conditions. Toward that goal, we have developed a finite element TCAD simulation model in Synopsis Sentaurus to allow for detailed exploration of varying perovskite absorber composition under a large range of illumination conditions. These simulations implement a 2D model of a high performing planar n–i–p perovskite solar cell architecture. The device is treated as a mesh, with higher meshing density applied at the interfaces to appropriately capture the physics of the charge transport layers. The Sentaurus model simulates the device optics under monochromatic illumination of varying wavelengths and intensities using a ray tracing method. A representative simulation output is shown schematically in Fig. 1(a), with the optical generation rate and electron current plotted for the perovskite absorber as a function of depth at the JSC condition for an illumination of 1 sun (AM 1.5G). The band diagram of the n–i–p architecture is shown in the inset of Fig. 1(b). The architecture consists of an ITO transparent electrode (150 nm) on a glass substrate with a SnO2 electron transport layer (ETL) of 15 nm, a poly(triaryl amine) (PTAA) hole transport layer (HTL) of 55 nm, and an Ag metal back electrode (90 nm). The Cs0.15FA0.85PbI3 absorber thickness was 500 nm. The film thicknesses in the simulation model are designed to match our experimentally fabricated n–i–p perovskite solar cells with Cs0.15FA0.85PbI3 absorbers. Further details of the material properties and assumptions used for the Sentaurus device model are provided in the supplementary material.

FIG. 1.

(a) TCAD device simulation output mapping optical generation and electron current density under 1 sun illumination at the JSC condition. (b) Simulated and experimental J–V characteristics for Cs0.15FA0.85PbI3 solar cells at 1 sun illumination. Inset: Band diagram illustrating n–i–p perovskite solar cell architecture used in this study. (c) Experimental J–V characteristics of perovskite solar cells under varied illumination intensities. (d) Simulated and experimental power conversion efficiency (PCE) as a function of illumination intensity for white light from ∼0.01–1 sun.

FIG. 1.

(a) TCAD device simulation output mapping optical generation and electron current density under 1 sun illumination at the JSC condition. (b) Simulated and experimental J–V characteristics for Cs0.15FA0.85PbI3 solar cells at 1 sun illumination. Inset: Band diagram illustrating n–i–p perovskite solar cell architecture used in this study. (c) Experimental J–V characteristics of perovskite solar cells under varied illumination intensities. (d) Simulated and experimental power conversion efficiency (PCE) as a function of illumination intensity for white light from ∼0.01–1 sun.

Close modal

Figures 1(b) and 1(c) show the simulated and experimentally demonstrated J–V curves for these n–i–p devices under a standard 1 sun illumination (AM 1.5G) as well as lower white light illumination intensity. The simulated and experimental results exhibit good agreement of their short circuit current (JSC) of ∼22.9 mA/cm2 and their open circuit voltage (VOC) of ∼1.06 V. The simulated device exhibits an efficiency of ∼20.2%, while the champion experimental device exhibits an efficiency of 18.1% at 1 sun illumination. The major difference between our experimental and theoretical results is the lower fill factor (FF) of the experimental device, a parameter widely known to correlate closely with the imperfect grain morphology and finite density of microscale defects in perovskite films.21 Table S1 shows a comparison of the photovoltaic parameters of the simulated and experimental cells, including the shunt and series resistance for these devices. These results show the lower shunt resistance (3.1 kΩ cm2 vs 5.4 kΩ cm2 for simulated cells) of the experimentally measured cells as well as a higher series resistance (5.3 Ω cm2 vs 2.1 Ω cm2 for simulated cells) that could result from resistive losses in transparent electrodes or charge transport layers. Our predicted theoretical efficiency of slightly above 20% for this planar n–i–p architecture is matched by several reported efficiencies for planar n–i–p perovskites with SnO2 as an ETL and PTAA as an HTL.22 This particular architecture is also chosen because SnO2 and PTAA represent a set of charge transport layers that are favorable for long term stability,23 an important criterion for deploying a reliable energy harvesting technology. Maximum power point stability tracking for n–i–p devices measured under 1 sun illumination is shown in Fig. S1.

Our TCAD device model allows for simulation of the perovskite solar cell efficiency under illumination across a wide range of wavelengths and intensities, which is essential for a WPT technology that may be implemented at variable distances. Figure 1(d) illustrates the simulated and experimentally demonstrated efficiency of this architecture under illumination from ∼0.005 suns to 1 sun intensity, corresponding to a power density range of 0.5–100 mW/cm2. The roll-off in efficiency for both simulation and experimental results at lower illumination intensities is a result of the non-radiative recombination processes, including interfacial and Shockley–Read–Hall (SRH) recombination. The practical impact of this recombination is that the theoretical power conversion efficiency (PCE) drops at lower incident intensities, for example, for red light, from 42.5% efficiency at a nominal intensity of 100 mW/cm2 (1 sun) to 37.5% at 1 mW/cm2 (0.01 suns). This is one of the fundamental challenges for low light energy harvesting with perovskites and other solar technologies.

The simulation model and experiments explore efficiency under various intensities and wavelengths of monochromatic illumination. Our model assumes LED illumination with spectral line widths of 16, 29, and 22 nm for red, green, and blue sources, respectively, to match our experimental conditions for monochromatic testing of perovskite solar cells. Our simulation model allows for extraction of J–V characteristics and efficiency [Fig. 2(a)] across a broad range of intensities from ∼0.1 mW to 100 mW/cm2, over which the reduction in VOC and fill factor (FF) results in approximately a 20% relative drop in efficiency. This range of illumination intensities corresponds to 0.001–1 sun of AM 1.5G. Our TCAD simulations predict that the planar n–i–p cells should have a maximum conversion efficiency above 42% for red (illumination Eph ∼ 1.97 eV), 35% for green (Eph ∼ 2.33 eV), and 31% for blue (Eph ∼ 2.64 eV). Our experimental measurements [Figs. 2(a)2(c)] show a similar trend in performance vs wavelength, with red illumination exhibiting the highest efficiency (above 33% at the range of higher incident power). Figure 2(a) shows the result of these measurements for a single perovskite cell, which exhibits slightly lower performance than the theoretical simulated device efficiency, but displays a comparable trend vs illumination intensity. Figure 2(b) summarizes the average efficiency at three selected intensities, showing a similar trend with red sources producing the highest efficiency and blue producing the lowest. Figure 2(c) shows representative JV curves obtained under different color illumination for a single device. Red shows substantially higher efficiency than blue and green illumination due to higher JSC at a nominal illumination intensity. Experimentally, green and blue illuminations generally result in efficiency in the range of 15%–25%, which is consistent with the mismatched photon energy relative to the perovskite bandgap energy.

FIG. 2.

(a) Plot of simulated (open symbols) and experimental (closed symbols) power conversion efficiency vs illumination intensity for red, green, and blue illumination. (b) Experimental averages, spreads, and trendlines of efficiencies for red, green, and blue light of varying intensities (N = 4 cells). Error bars indicate one standard deviation. (c) Measured J–V curves of a perovskite cell tested under red, green, and blue LED illumination at the specified center wavelengths with intensities of 30.2, 33.5, and 26.4 mW/cm2, respectively.

FIG. 2.

(a) Plot of simulated (open symbols) and experimental (closed symbols) power conversion efficiency vs illumination intensity for red, green, and blue illumination. (b) Experimental averages, spreads, and trendlines of efficiencies for red, green, and blue light of varying intensities (N = 4 cells). Error bars indicate one standard deviation. (c) Measured J–V curves of a perovskite cell tested under red, green, and blue LED illumination at the specified center wavelengths with intensities of 30.2, 33.5, and 26.4 mW/cm2, respectively.

Close modal

The peak efficiency we observe with red illumination, however, can exceed 33%, with high intensity illumination leading to generation of greater than 20 mW/cm2. The relative variation in efficiency vs wavelength is primarily due to the mismatch between the LED photon energy and the nominal bandgap energy of the perovskite. Red LEDs provide the closest spectral match to our double cation perovskite, which should allow for the highest WPT efficiency. Beyond the bandgap matching effect, the EQE for perovskites varies across the visible range depending on the optical properties of the device stack, such as the parasitic absorption in bottom contact layers and reflections at these interfaces. Experimentally, the peak EQE is usually found near blue, at 400–510 nm.24 Recent studies have shown that the highly efficient coupling of light in this wavelength range is due to the high refractive index of the perovskite film, which maximizes transmittance and minimizes reflectance at the interface with the transparent electrodes. The higher EQE at shorter wavelengths may partially compensate the drop in WPT efficiency in cases where there is a mismatch in the bandgap and illumination photon energies.

Based on our experimental results, we consider how the composition of a metal halide perovskite absorber can be optimized for monochromatic illumination and, conversely, how the illumination can be effectively matched to the absorber's properties. In simulation, we characterized the PCE of perovskite solar cells with the same nominal architecture (SnO2/perovskite/PTAA) but with different absorber bandgaps. This has been done experimentally by varying the perovskite composition via engineering of the anion composition (Br vs I).25, Figure 3(a) shows a map of the simulated PCE as a function of intensity and illumination wavelength, which is highest (PCE > 50%) for longer wave sources approaching the bandgap energy of the cell. Compositional engineering of the perovskite absorber provides a method to shift the optimum efficiency toward lower wavelengths corresponding to those of high-efficiency, high-power optical sources. Figure 3(b) illustrates this trend across the range of typical achievable bandgaps for perovskite cells, showing the theoretical potential for achieving PCE above 60% with wide bandgap perovskite absorbers. This is a similar conclusion to theoretical predictions by Freunek et al., suggesting that perovskite bandgaps of 1.90–2.00 eV could be optimum for narrow band artificial indoor lighting.26 The Gaussians inset on the left side of Figs. 3(a) and 3(b) indicate the spread of wavelengths emitted by the LEDs in our experimental setup.

FIG. 3.

(a) Predicted power conversion efficiency for perovskite solar cells at different wavelengths and light intensities for a perovskite with a bandgap energy of 1.55 eV. (b) Predicted power conversion efficiency for perovskite solar cells at different wavelengths and bandgap energies (ranging from 1.5 to 2.1 eV) at an illumination intensity of 100 mW/cm2. The Gaussian distributions on the left indicate the wavelengths of the LEDs used to measure performance in this work (627, 532, and 450 nm).

FIG. 3.

(a) Predicted power conversion efficiency for perovskite solar cells at different wavelengths and light intensities for a perovskite with a bandgap energy of 1.55 eV. (b) Predicted power conversion efficiency for perovskite solar cells at different wavelengths and bandgap energies (ranging from 1.5 to 2.1 eV) at an illumination intensity of 100 mW/cm2. The Gaussian distributions on the left indicate the wavelengths of the LEDs used to measure performance in this work (627, 532, and 450 nm).

Close modal
We apply our simulation model to predict the efficiency of perovskite cells as WPT receivers under varying monochromatic illumination wavelength and intensity. This information allows for modeling of the perovskite solar cell’s capacity for power transfer at varying distances via illumination by a light source, such as an LED or laser. We consider the case of a wireless IoT device with a size in the range of ∼25 cm2 that would otherwise be powered by a single use coin cell battery. We predict a nominal system-level wireless power transfer efficiency as a function of distance between the source and receiver, as depicted in Fig. 4(a). Figure 4(b) shows the theoretical efficiency for our red, green, and blue light illuminated perovskite solar cells as a function of distance. We first consider the short-range regime of WPT for which the illumination spot size remains smaller than the dimension of the solar cell. This regime applies at a distance of ∼10–1000× the cell side, corresponding to about 0.5–100 m. In this range, the predicted efficiency based on TCAD simulations is 35%–45%, varying with the wavelength of the laser with the same trends as the color comparison presented in Fig. 2. The efficiency also decreases weakly with distance in this short-range regime as the effective incident intensity is weakened by the increase in the spot size. At greater distances determined by the beam divergence (θ) of the source, the system transitions to the long-range regime of WPT, for which the receiver efficiency scales down according to an inverse square law,
η1(DD0)2.
(1)
FIG. 4.

(a) Schematic illustrating the angular spread of a monochromatic light source, such as a laser, incident on a solar cell, compared with induction wireless power transfer over the same distance d. Both the coils and solar cell have side length L. (b) Predicted efficiency of perovskite solar cells under varied wavelength illumination, compared to induction. (c) J–V curves measured experimentally for a silicon control cell and our perovskite cell at 4 m with illumination by a red laser at an incident intensity of 23 mW/cm2. (d) Efficiency vs distance for laser illuminated perovskite and Si solar cells at a distance of 4–105 m. (e) Setup for long-range wireless power transfer with a perovskite solar cell and a red laser.

FIG. 4.

(a) Schematic illustrating the angular spread of a monochromatic light source, such as a laser, incident on a solar cell, compared with induction wireless power transfer over the same distance d. Both the coils and solar cell have side length L. (b) Predicted efficiency of perovskite solar cells under varied wavelength illumination, compared to induction. (c) J–V curves measured experimentally for a silicon control cell and our perovskite cell at 4 m with illumination by a red laser at an incident intensity of 23 mW/cm2. (d) Efficiency vs distance for laser illuminated perovskite and Si solar cells at a distance of 4–105 m. (e) Setup for long-range wireless power transfer with a perovskite solar cell and a red laser.

Close modal

In Eq. (1), D is the source-to-receiver distance and D0 is the transition distance at which the laser beam spot size begins to exceed the size of the cell. For simplicity, we assume a single perovskite cell rather than a module so that we can ignore the impact of non-uniform illumination patterns in these two distance regimes. We specifically note that lower resistive losses at lower illumination intensities as well as the lower manufacturing costs could make individual cells rather than modules a promising solution for many new low-power IoT nodes, which have been designed to run at ultra-low voltages below 1 V.27 This architecture mitigates the need for additional power management circuits and eliminates the necessary dead areas inherent in implementations of cell to cell module interconnection. Compared with the efficiency of resonant inductive wireless power transfer shown in the black curve in Fig. 4(b), our photonic wireless power transfer extends the effective range for appreciable power transmission by 10–100×, at which wireless power transfer by other methods is highly inefficient.

Our experimental results demonstrate the potential for high-efficiency medium-range wireless power transfer as well as long-range power transmission at a distance of greater than 100 m. For illumination by a red laser (λ = 627 nm), our double cation perovskite cells offer an efficiency of greater than 30% at short range and 18% at long range. Figure 4(c) shows the J–V curves for our perovskite solar cells as well as high-efficiency commercial Silicon solar cells measured under identical illumination conditions (23 mW/cm2 intensity of red light). The perovskite cells exhibit a power output that is 70% higher than Si under these conditions, primarily because of the voltage (VOC) output that is 70%–80% higher across a range of distances. The resulting advantage in efficiency [Fig. 4(d)] is maintained at a distance of 4 m as well as the long-range measurements beyond 100 m, for which the experimental setup is shown in Fig. 4(e). The efficiency of both cells decreases at larger distances corresponding to lower incident intensities, but the perovskite cell maintains a significant advantage, offering greater than 15% efficiency at beyond 100 m. This significant performance advantage offered by perovskites (EG ∼ 1.5–2.3 eV) can be understood first by considering its bandgap compared with Si (EG ∼ 1.12 eV). The wider perovskite bandgap ensures that these absorbers can provide a tighter spectral match with high-efficiency monochromatic light sources.

For a 5 cm nominal cell size, the effective range is considered to be ∼200 m, though we acknowledge that this approach requires line of sight transmission, which may not be achievable in every application. Although we acknowledge the potential safety challenges of laser illumination at high intensities, we highlight the possibility for LED-based illumination at short- and medium-range applications, as has been recently demonstrated with III–V GaAs cells.28 

The potential for strong spectral matching as well as the lower manufacturing cost and light weight could allow perovskites to extend the practical range of distances available for WPT. Device area scaling (e.g., 1–10 cm2), in particular, provides an opportunity for harvesting sufficient power at lower illumination intensities for trickle charging energy storage elements, such as batteries29 or supercapacitors17 integrated into an IoT device. Figure 5(a) shows a comparison of existing prototypes of technologies from the energy harvesting literature. The reported power per weight and power per area are compared for piezoelectric, thermoelectric, and triboelectric generators, as well as magnetic inductive WPT and photonic WPT with perovskite solar cells (references for each data point in this comparison are provided in Fig. S2). A benchmark power per weight for lithium-ion batteries (specific power) is indicated with the pink dashed line. Based on the comparison, it can be seen that perovskite solar cells such as those demonstrated in this work can offer an optimal trade-off of high power per weight (1–10 W/g) as well as high power per area (1–10 mW/cm2). For perovskite cells integrated on ultrathin substrates, this could offer a comparable or higher power per weight than the state-of-the-art lithium-ion batteries.29–31 With most Li-ion batteries having an energy density of 250–300 Wh/kg,30 at a small-scale IoT power consumption of 0.1 W, every gram of battery weight would provide 150–180 min of charge for continuous device operation. Assuming a typical coin battery weight of 3 g and a duty cycle of 1% (e.g., an IoT device that spends 99% of the time in a low power sleep state), the device would be able to operate for ∼35 days before the battery must be replaced or recharged. PVSK photonic energy harvesting, on the other hand, could power such devices over the long term, assuming continued progress beyond the current state of the art of 3000–5000 h operational stability.32 This longevity would eliminate the need for recharging batteries or single use batteries, a significant factor limiting the environmental and economic sustainability of IoT technologies.27 

FIG. 5.

(a) Comparison of power per device area and working range limits for various WPT technologies from literature review of applied energy harvesting prototypes. Full references for this comparison are provided in an annotated version shown in Fig. S2. (b) Comparison of maximum working range for a variety of WPT technologies, where direct contact is required for piezoelectric, thermoelectric, and triboelectric systems. The distance shown for inductive WPT is the calculated drop-off distance at which the efficiency falls below 10% for a 5 cm diameter coil, and the perovskite solar cell (PSC) distance shown here reflects the WPT demonstration at >100 m in this work.

FIG. 5.

(a) Comparison of power per device area and working range limits for various WPT technologies from literature review of applied energy harvesting prototypes. Full references for this comparison are provided in an annotated version shown in Fig. S2. (b) Comparison of maximum working range for a variety of WPT technologies, where direct contact is required for piezoelectric, thermoelectric, and triboelectric systems. The distance shown for inductive WPT is the calculated drop-off distance at which the efficiency falls below 10% for a 5 cm diameter coil, and the perovskite solar cell (PSC) distance shown here reflects the WPT demonstration at >100 m in this work.

Close modal

Importantly, as shown in Fig. 5(b), our photonic wireless power transfer can offer ranges that are 10–500× larger than inductive wireless power transfer. Figure 5(b) compares the working ranges for various energy harvesting technologies, with direct contact assumed for triboelectric, thermoelectric, and piezoelectric generators (distances under 1 cm). The distances for magnetic induction and perovskite solar cells are based on similar assumptions to the analysis presented earlier, assuming a similar solar cell size and inductive coil size of ∼5 cm compatible with most miniaturized IoT devices. The ability to print larger area perovskite solar cells via methods such as high-speed flexography33,34 and modules (e.g., 10 × 10 cm2) at low cost also opens the possibility for extending the possible WPT range beyond 100 m without imposing any need for additional optical components, a key attribute for maintaining low weight and system-level mechanical flexibility. Finally, we note additional opportunities for improving device design for optimizing efficiency of medium and long range photonic WPT with perovskites. The quadratic scaling of resistive losses with current motivates the use of thinner, more transmissive window electrodes for devices operating at lower illumination intensities. This provides the opportunity to utilize alternative transparent conductive materials, such as PEDOT:PSS, Ag NWs, or other materials deposited by low-cost printing methods without sacrificing device efficiency.

In summary, this study analyzes the potential for perovskite solar cells as an effective receiver for photonic wireless power transmission. We experimentally demonstrate the high efficiency of perovskite solar cells under monochromatic illumination over a wide range of intensities that could be relevant to charging wireless devices for applications such as the Internet of Things (IoT). Our simulation model provides insights into the theoretically achievable performance that could be possible with composition tuning of the perovskite absorber to improve the spectral match with common monochromatic sources, including LEDs and lasers. Finally, we present calculations comparing the efficiency of short- and medium-range WPT with perovskite solar cells against the state-of-the art inductive WPT and experimentally demonstrate efficient, perovskite-based WPT at a distance of greater than 100 m.

A more detailed description of the TCAD simulation design and the calculations for efficiency vs distance shown in Fig. 4(b) can be found in the supplementary material. The Sentaurus numerical meshing scheme is also shown in Fig. S3 in the supplementary material. Material and optical parameters used for the TCAD simulations are given in Tables S3 and S4.

Spin coated SnO2 solutions were formulated from a 3 wt. % SnO2 nanoparticle solution in deionized (DI) H2O. The double cation perovskite solution consisted of 0.0546 g CsI (>99.0% from TCI Chemicals), 0.2108 g FAI (GreatCellSolar Materials), and 0.6454 g PbI2 (99.9985% Alfa Aesar) dissolved in 1 ml of solvent with a 3:1 v/v ratio of n-dimethylformamide(DMF):dimethyl sulfoxide (DMSO). Anhydrous DMF and DMSO were both obtained from ACROS Organics and stored in an inert nitrogen glovebox where the perovskite solution was also prepared and stored. Once mixed, the perovskite solution was stirred overnight at 65 °C to dissolve. PTAA was mixed at a concentration of 15 mg/ml in toluene and co-doped with Li-TFSI and t-BP at a ratio of 15 µl/1 ml solution for each dopant. The PTAA solution was also stirred overnight at 65 °C to dissolve.

SnO2 ETLs were deposited on cleaned ITO either by spin coating 25 µl of solution at 4000 rpm for 30 s. Following deposition, the SnO2 NP films were annealed at 150 °C for 1 h in air and then moved into an inert nitrogen glovebox for perovskite deposition. Spin coated SnO2 films had a thickness of ∼15 nm as measured by stylus profilometry (Tencor Alphastep D-500). 20 µl of the perovskite solution was spread onto the substrates and spin coated at 5000 rpm for 55 s. When there were 15 s remaining in the spin, 300 µl of ethyl acetate (EA) antisolvent was deposited. After spinning, the devices were transferred to a hotplate where they were annealed for 30 min at 100 °C. Following the perovskite anneal, the samples were cooled to room temperature and the HTLs were coated by spinning 25 µl of PTAA solution for 30 s at 5000 rpm. The cells were completed by thermally evaporating a 100 nm thick Ag metal electrode. These devices were not encapsulated or sealed from ambient atmosphere. The devices’ performance characteristics are summarized in Table S2 in the supplementary material.

The perovskite solar cells were measured in ambient conditions (≈45% RH, 25 °C) under 1 sun, AM 1.5G illumination (Oriel LSH-7320 ABA LED Solar Simulator). The devices were fabricated with a pixel area of 0.134 cm2. The lamp intensity was set based on an NREL calibrated Si reference cell. J–V curves were collected with a precision sourcemeter (B2902A) measured between −0.2 and 1.2 V with an increment of 0.01 V and a delay of 0.06 s between points. Monochromatic testing was performed using blue, green, and red Luxeon Rebel Color LEDs focused with a 10048 Carclo Lens. The apparatus was seated on a 3D printed stand and driven at constant currents using an Agilent B2902A precision sourcemeter. The intensity of the LED at each measurement was recorded using an 843-R optical power meter coupled with an 818-UV/DB Optical power detector. The exact LED emission center wavelength and linewidth, as required by the powermeter, were found using a UV–vis spectrophotometer (Ocean optics USB 2000+). A two-axis micrometer sliding stage was used to center the LED beam over the solar cell or power detector before each J–V or intensity measurement, respectively, which ensured that the powermeter and solar cell were both subject to the same illumination intensity. Cooling was used to avoid droop in the LED optical power output at high drive currents.

Long distance power transmission measurements were performed indoors using a Alpec Red 650 nm Laser Pointer PN 4000 as the source. The total optical power output of the laser was measured to be 3.25 mW. An IXYS Silicon Solar Cell KX0B22-12X1 F, masked to an area of 0.134 cm2 to match the perovskite cells, was used as a control. The laser pointer was aimed at the solar cells by direct line of sight at a distance of ∼105 m and then at shorter distances of 25 and 4 m. J–V curves were measured for the cells under illuminated and dark conditions by the same procedure as described earlier. The maximum power points of the cells were divided by the intensity of incident laser light to obtain the effective power conversion efficiency for both the silicon and perovskite cells.

The supplementary material includes a discussion of the Sentaurus TCAD model physics, meshing, material parameters used in the model, and full calculations for comparisons of WPT efficiency; full details of the references in the literature comparison in Fig. 5 are also provided.

We acknowledge kind assistance from the Liu and Fossum groups at Dartmouth. Julia E. Huddy was supported by a U.S. Department of Education Graduate Assistance in Areas of National Need (GAANN) fellowship.

The authors have no conflicts to disclose.

F.V.G. and M.I.T. contributed equally to this work.

Matthew I. Timofeev: Conceptualization (supporting); Data curation (equal); Formal analysis (equal); Investigation (lead); Methodology (equal); Visualization (equal); Writing – original draft (supporting); Writing – review & editing (supporting). Francesco V. Guarnieri: Conceptualization (supporting); Formal analysis (equal); Investigation (equal); Writing – original draft (supporting); Writing – review & editing (supporting). Julia E. Huddy: Investigation (supporting); Methodology (supporting); Writing – original draft (supporting); Writing – review & editing (supporting). William J. Scheideler: Conceptualization (equal); Data curation (equal); Formal analysis (equal); Funding acquisition (lead); Investigation (supporting); Project administration (lead); Resources (lead); Supervision (lead); Visualization (supporting); Writing – original draft (lead); Writing – review & editing (lead).

The data that support the findings of this study are available within the article and its supplementary material.

1.
N.
Rolston
,
W. J.
Scheideler
,
A. C.
Flick
,
J. P.
Chen
,
H.
Elmaraghi
,
A.
Sleugh
,
O.
Zhao
,
M.
Woodhouse
, and
R. H.
Dauskardt
, “
Rapid open-air fabrication of perovskite solar modules
,”
Joule
4
(
12
),
2675
2692
(
2020
).
2.
F.
Schackmar
,
H.
Eggers
,
M.
Frericks
,
B. S.
Richards
,
U.
Lemmer
,
G.
Hernandez-Sosa
, and
U. W.
Paetzold
, “
Perovskite solar cells with all-inkjet-printed absorber and charge transport layers
,”
Adv. Mater. Technol.
6
(
2
),
2000271
(
2021
).
3.
M.
Kaltenbrunner
,
G.
Adam
,
E. D.
Głowacki
,
M.
Drack
,
R.
Schwödiauer
,
L.
Leonat
,
D. H.
Apaydin
,
H.
Groiss
,
M. C.
Scharber
,
M. S.
White
,
N. S.
Sariciftci
, and
S.
Bauer
, “
Flexible high power-per-weight perovskite solar cells with chromium oxide–metal contacts for improved stability in air
,”
Nat. Mater.
14
(
10
),
1032
1039
(
2015
).
4.
L.
Portilla
,
K.
Loganathan
,
H.
Faber
,
A.
Eid
,
J. G. D.
Hester
,
M. M.
Tentzeris
,
M.
Fattori
,
E.
Cantatore
,
C.
Jiang
,
A.
Nathan
,
G.
Fiori
,
T.
Ibn-Mohammed
,
T. D.
Anthopoulos
, and
V.
Pecunia
, “
Wirelessly powered large-area electronics for the Internet of Things
,”
Nat. Electron.
6
(
1
),
10
17
(
2023
).
5.
J.
Min
,
S.
Demchyshyn
,
J. R.
Sempionatto
,
Y.
Song
,
B.
Hailegnaw
,
C.
Xu
,
Y.
Yang
,
S.
Solomon
,
C.
Putz
,
L. E.
Lehner
,
J. F.
Schwarz
,
C.
Schwarzinger
,
M. C.
Scharber
,
E.
Shirzaei Sani
,
M.
Kaltenbrunner
, and
W.
Gao
, “
An autonomous wearable biosensor powered by a perovskite solar cell
,”
Nat. Electron.
6
(
8
),
630
641
(
2023
).
6.
Y.
Song
,
J.
Min
,
Y.
Yu
,
H.
Wang
,
Y.
Yang
,
H.
Zhang
, and
W.
Gao
, “
Wireless battery-free wearable sweat sensor powered by human motion
,”
Sci. Adv.
6
(
40
),
eaay9842
(
2020
).
7.
X.
Zeng
,
R.
Peng
,
Z.
Fan
, and
Y.
Lin
, “
Self-powered and wearable biosensors for healthcare
,”
Mater. Today Energy
23
,
100900
(
2022
).
8.
R.
Cheng
,
C.-C.
Chung
,
H.
Zhang
,
F.
Liu
,
W.-T.
Wang
,
Z.
Zhou
,
S.
Wang
,
A. B.
Djurišić
, and
S.-P.
Feng
, “
Tailoring triple-anion perovskite material for indoor light harvesting with restrained halide segregation and record high efficiency beyond 36%
,”
Adv. Energy Mater.
9
(
38
),
1901980
(
2019
).
9.
X.
Guo
,
X.
Chen
,
Q.
Li
,
G.
Zhang
,
G.
Ding
,
F.
Li
,
Y.
Shi
,
Y.
Zhang
,
H.
Wang
,
Y.
Zheng
, and
Y.
Shao
, “
High-efficiency wide-bandgap perovskite solar cells for laser energy transfer underwater
,”
Energy Technol.
11
(
7
),
2300083
(
2023
).
10.
M.
Li
,
C.
Zhao
,
Z.-K.
Wang
,
C.-C.
Zhang
,
H. K. H.
Lee
,
A.
Pockett
,
J.
Barbé
,
W. C.
Tsoi
,
Y.-G.
Yang
,
M. J.
Carnie
,
X.-Y.
Gao
,
W.-X.
Yang
,
J. R.
Durrant
,
L.-S.
Liao
, and
S. M.
Jain
, “
Interface modification by ionic liquid: A promising candidate for indoor light harvesting and stability improvement of planar perovskite solar cells
,”
Adv. Energy Mater.
8
(
24
),
1801509
(
2018
).
11.
D. H.
Nguyen
,
G.
Tumen-Ulzii
,
T.
Matsushima
, and
C.
Adachi
, “
Performance analysis of a perovskite-based thing-to-thing optical wireless power transfer system
,”
IEEE Photonics J.
14
(
1
),
6213208
(
2022
).
12.
J.
Prummel
,
M.
Papamichail
,
J.
Willms
,
R.
Todi
,
W.
Aartsen
,
W.
Kruiskamp
,
J.
Haanstra
,
E.
Opbroek
,
S.
Rievers
,
P.
Seesink
,
J.
van Gorsel
,
H.
Woering
, and
C.
Smit
, “
A 10 mW bluetooth low-energy transceiver with on-chip matching
,”
IEEE J. Solid-State Circuits
50
(
12
),
3077
3088
(
2015
).
13.
J.
Tosi
,
F.
Taffoni
,
M.
Santacatterina
,
R.
Sannino
, and
D.
Formica
, “
Performance evaluation of bluetooth low energy: A systematic review
,”
Sensors
17
(
12
),
2898
(
2017
).
14.
Y.
Katsuta
and
T.
Miyamoto
, “
Design and experimental characterization of optical wireless power transmission using GaAs solar cell and series-connected high-power vertical cavity surface emitting laser array
,”
Jpn. J. Appl. Phys.
57
(
8S2
),
08PD01
(
2018
).
15.
J.
Kim
,
J.
Seo
,
D.
Jung
,
T.
Lee
,
H.
Ju
,
J.
Han
,
N.
Kim
,
J.
Jeong
,
S.
Cho
,
J. H.
Seol
, and
J.
Lee
, “
Active photonic wireless power transfer into live tissues
,”
Proc. Natl. Acad. Sci. U. S. A.
117
(
29
),
16856
16863
(
2020
).
16.
A. W. S.
Putra
,
H.
Kato
, and
T.
Maruyama
, in
2020 IEEE PELS Workshop on Emerging Technologies: Wireless Power Transfer (WoW)
(
IEEE
,
2020
), pp.
374
376
.
17.
J. S.
Ward
,
T.
Remo
,
K.
Horowitz
,
M.
Woodhouse
,
B.
Sopori
,
K.
VanSant
, and
P.
Basore
, “
Techno-economic analysis of three different substrate removal and reuse strategies for III-V solar cells
,”
Prog. Photovoltaics
24
(
9
),
1284
1292
(
2016
).
18.
M. A. N.
Perera
,
M.
Katz
,
J.
Häkkinen
, and
R.
Godaliyadda
, “
Light-based IoT: Developing a full-duplex energy autonomous IoT node using printed electronics technology
,”
Sensors
21
(
23
),
8024
(
2021
).
19.
R.
Ishikawa
,
T.
Kato
,
R.
Anzo
,
M.
Nagatake
,
T.
Nishimura
,
N.
Tsuboi
, and
S.
Miyajima
, “
Widegap CH3NH3PbBr3 solar cells for optical wireless power transmission application
,”
Appl. Phys. Lett.
117
(
1
),
013902
(
2020
).
20.
S. N. R.
Kantareddy
,
I.
Mathews
,
S.
Sun
,
M.
Layurova
,
J.
Thapa
,
J.-P.
Correa-Baena
,
R.
Bhattacharyya
,
T.
Buonassisi
,
S. E.
Sarma
, and
I. M.
Peters
, “
Perovskite PV-powered RFID: Enabling low-cost self-powered IoT sensors
,”
IEEE Sens. J.
20
(
1
),
471
478
(
2020
).
21.
J.
Shi
,
F.
Li
,
C.
Liu
,
X.
Ling
,
X.
Zhang
,
Y.
Wang
,
J.
Guo
,
C.
Zhao
,
D.
Wang
,
Y.
Li
,
W.
Ma
,
J.
Yuan
, and
B.
Xu
, “
Inverted perovskite solar cells with >85% fill factor via sequential interfacial engineering
,”
Sol. RRL
7
(
11
),
2300078
(
2023
).
22.
Q.
Jiang
,
X.
Zhang
, and
J.
You
, “
SnO2: A wonderful electron transport layer for perovskite solar cells
,”
Small
14
(
31
),
1801154
(
2018
).
23.
J.
Chen
,
X.
Zhao
,
S.-G.
Kim
, and
N.-G.
Park
, “
Multifunctional chemical linker imidazoleacetic acid hydrochloride for 21% efficient and stable planar perovskite solar cells
,”
Adv. Mater.
31
(
39
),
1902902
(
2019
).
24.
K. O.
Brinkmann
,
T.
Becker
,
F.
Zimmermann
,
C.
Kreusel
,
T.
Gahlmann
,
T.
Haeger
, and
T.
Riedl
, “
The optical origin of near-unity external quantum efficiencies in perovskite solar cells
,”
Sol. RRL
5
(
9
),
2100371
(
2021
).
25.
K. A.
Bush
,
K.
Frohna
,
R.
Prasanna
,
R. E.
Beal
,
T.
Leijtens
,
S. A.
Swifter
, and
M. D.
McGehee
, “
Compositional engineering for efficient wide band gap perovskites with improved stability to photoinduced phase segregation
,”
ACS Energy Lett.
3
(
2
),
428
435
(
2018
).
26.
M.
Freunek
,
M.
Freunek
, and
L. M.
Reindl
, “
Maximum efficiencies of indoor photovoltaic devices
,”
IEEE J. Photovoltaics
3
(
1
),
59
64
(
2013
).
27.
V.
Pecunia
,
L. G.
Occhipinti
, and
R. L. Z.
Hoye
, “
Emerging indoor photovoltaic technologies for sustainable Internet of Things
,”
Adv. Energy Mater.
11
(
29
),
2100698
(
2021
).
28.
F.
Peña-Camargo
,
P.
Caprioglio
,
F.
Zu
,
E.
Gutierrez-Partida
,
C. M.
Wolff
,
K.
Brinkmann
,
S.
Albrecht
,
T.
Riedl
,
N.
Koch
,
D.
Neher
, and
M.
Stolterfoht
, “
Halide segregation versus interfacial recombination in bromide-rich wide-gap perovskite solar cells
,”
ACS Energy Lett.
5
(
8
),
2728
2736
(
2020
).
29.
H.
Budde-Meiwes
,
J.
Drillkens
,
B.
Lunz
,
J.
Muennix
,
S.
Rothgang
,
J.
Kowal
, and
D. U.
Sauer
, “
A review of current automotive battery technology and future prospects
,”
Proc. Inst. Mech. Eng., Part D
227
(
5
),
761
776
(
2013
).
30.
Y.-T.
Liu
,
S.
Liu
,
G.-R.
Li
, and
X.-P.
Gao
, “
Strategy of enhancing the volumetric energy density for lithium–sulfur batteries
,”
Adv. Mater.
33
(
8
),
2003955
(
2021
).
31.
Z. P.
Cano
,
D.
Banham
,
S.
Ye
,
A.
Hintennach
,
J.
Lu
,
M.
Fowler
, and
Z.
Chen
, “
Batteries and fuel cells for emerging electric vehicle markets
,”
Nat. Energy
3
(
4
),
279
289
(
2018
).
32.
G.
Du
,
L.
Yang
,
P.
Dong
,
L.
Qi
,
Y.
Che
,
X.
Wang
,
X.
Zhang
, and
J.
Zhang
, “
Sequential molecule-doped hole conductor to achieve >23% perovskite solar cells with 3000-hour operational stability
,”
Adv. Mater.
35
(
35
),
2303692
(
2023
).
33.
J. E.
Huddy
,
Y.
Ye
, and
W. J.
Scheideler
, “
Eliminating the perovskite solar cell manufacturing bottleneck via high-speed flexography
,”
Adv. Mater. Technol.
7
(
7
),
2101282
(
2022
).
34.
J. E.
Huddy
and
W. J.
Scheideler
, “
Rapid 2D patterning of high-performance perovskites using large area flexography
,”
Adv. Funct. Mater.
2306312
(published online).

Supplementary Material