Optical upconversion is a net process by which two low energy photons are converted into one higher energy photon. There is vast potential to exploit upconversion in applications ranging from solar energy and biological imaging to data storage and photocatalysis. Here, we link two upconverting chromophores together to synthesize a series of novel tetracene dimers for use as annihilators. When compared with the monomer annihilator, TIPS–tetracene, the dimers yield a strong enhancement in the triplet fusion process, also known as triplet–triplet annihilation, as demonstrated via a large increase in upconversion efficiency and an order of magnitude reduction of the threshold power for maximum yield. Along with the ongoing rapid improvements to sensitizer materials, the dimerization improvements demonstrated here open the way to a wide variety of emerging upconversion applications.
Triplet fusion upconversion, the conversion of two low-energy photons into one higher-energy photon via excitonic intermediates, has the potential to revolutionize fields as diverse as biological imaging, photovoltaics, and optogenetics. However, important hurdles to widespread application still exist; for example, the vast majority of demonstrations are in nonpolar solvents, limiting applications. Furthermore, the necessary high concentrations of dyes limit optical penetration depth. Efforts toward aqueous solutions utilizing micelles and other nanoencapsulants have been limited by poor efficiencies or scatter from the nanoparticles. Here, we demonstrate a facile micellular fabrication method that drives a high boiling point solvent into the core of a block copolymer micelle, greatly reducing molecular aggregation. We show that this simple preparation is scalable and provides benefits across five different colors of photon upconversion. We expect this simple, user-friendly, and high-performance system to aid a multitude of photon upconversion applications, in particular, for optogenetics, photodynamic therapy, and photochemistry.
Noninvasive behavioral tracking of animals during experiments is critical to many scientific pursuits. Extracting the poses of animals without using markers is often essential to measuring behavioral effects in biomechanics, genetics, ethology, and neuroscience. However, extracting detailed poses without markers in dynamically changing backgrounds has been challenging. We recently introduced an open-source toolbox called DeepLabCut that builds on a state-of-the-art human pose-estimation algorithm to allow a user to train a deep neural network with limited training data to precisely track user-defined features that match human labeling accuracy. Here, we provide an updated toolbox, developed as a Python package, that includes new features such as graphical user interfaces (GUIs), performance improvements, and active-learning-based network refinement. We provide a step-by-step procedure for using DeepLabCut that guides the user in creating a tailored, reusable analysis pipeline with a graphical processing unit (GPU) in 1–12 h (depending on frame size). Additionally, we provide Docker environments and Jupyter Notebooks that can be run on cloud resources such as Google Colaboratory.
Interest in organic–inorganic hybrid perovskite (ABX3) LEDs has exploded over the past several years, yet significant gains in stability, efficiency, and brightness are required before commercialization is possible, particularly for blue devices. The perovskite composition has been shown to play a crucial role in its performance, yet to date nearly all existing reports focus on tuning the A-site composition. Here, we find that doping the B-site with manganese allows us to achieve bright, efficient, and stable LEDs regardless of A or X composition. By doping with Mn, we demonstrate ultrabright sky-blue, green, and red perovskite LEDs with a maximum brightness of 11800, 97000, and 1470 cd/m2 and quantum efficiencies of 0.58%, 3.2%, and 5.1%, respectively. Crucially, these devices show excellent operational stability, with the sky-blue devices lasting for 20 min and red devices over 5 h with strong spectral stability. Moreover, the green devices showed over 1% efficiency even at higher current densities, ∼2000 mA/cm2. Mn doping allows for universal improvement in perovskite performance and stability, opening the door to a huge number of applications.
Optical upconversion based on triplet fusion (TF), also known as triplet–triplet annihilation, is a process by which two or more low-energy photons are converted to one higher energy photon. This process requires two components, a sensitizer which absorbs the incident low-energy photons and an annihilator which emits the higher energy photons. While much attention has been given to the investigation of new types of sensitizers, very little work has been done on the exploration of new annihilators. In this work, we show that the singlet energy of diketopyrrolopyrroles (DPPs) can be altered by modifying the pendant aryl substituents to the core. This allows us to meet the energetic requirements necessary for TF upconversion and demonstrates DPPs as a new class of annihilator molecules. Using this new DPP platform, the output wavelength from upconversion can easily be tuned, which will greatly diversify the number of applications of DPPs in upconversion technologies.
Recent advances in photoredox catalysis have made it possible to achieve various challenging synthetic transformations, polymerizations and surface modifications1,2,3. All of these reactions require ultraviolet- or visible-light stimuli; however, the use of visible-light irradiation has intrinsic challenges. For example, the penetration of visible light through most reaction media is very low, leading to problems in large-scale reactions. Moreover, reactants can compete with photocatalysts for the absorption of incident light, limiting the scope of the reactions. These problems can be overcome by the use of near-infrared light, which has a much higher penetration depth through various media, notably biological tissue4. Here we demonstrate various photoredox transformations under infrared radiation by utilizing the photophysical process of triplet fusion upconversion, a mechanism by which two low-energy photons are converted into a higher-energy photon. We show that this is a general strategy applicable to a wide range of photoredox reactions. We tune the upconversion components to adjust the output light, accessing both orange light and blue light from low-energy infrared light, by pairwise manipulation of the sensitizer and annihilator. We further demonstrate that the annihilator itself can be used as a photocatalyst, thus simplifying the reaction. This approach enables catalysis of high-energy transformations through several opaque barriers using low-energy infrared light.
Scaling up electrochemical water splitting is nowadays in high demand for hydrogen economy implementation. Tremendous eff orts over the past decade have been focused on exploring alternative catalytic materials, including a variety of earth-abundant transitionmetal-based catalysts, to replace traditional noble metals such as Pt, Ir, or Ru. Nevertheless, few eff orts have been carried out for (1) scalable catalyst synthesis on current collectors and (2) practical device design toward large-scale H2 generation. Herein, we designed a modular alkaline water-splitting electrolyzer system with scaled-up metal foam electrodes covered by low-cost NiMo alloy and Ni3 Fe oxide for efficient hydrogen evolution and oxygen evolution, respectively. An electrolyte circulation system facilitates the mass transport and thus can further boost the H2 generation particularly under large currents. As a result, the overall water-splitting performance of one-unit cell with a dimension of 10 Å~ 10 cm2 under room temperature presents an early onset voltage of 1.54 V and delivered practical currents of 20 and 55 A (9.1 and 25.0 L/h H2 generation) under 2.2 and 2.9 V without iR compensations, respectively. This demonstration could stimulate new focuses in water splitting toward more practical applications.
Quantifying behavior is crucial for many applications in neuroscience. Videography provides easy methods for the observation and recording of animal behavior in diverse settings, yet extracting particular aspects of a behavior for further analysis can be highly time consuming. In motor control studies, humans or other animals are often marked with reflective markers to assist with computer-based tracking, but markers are intrusive, and the number and location of the markers must be determined a priori. Here we present an efficient method for markerless pose estimation based on transfer learning with deep neural networks that achieves excellent results with minimal training data. We demonstrate the versatility of this framework by tracking various body parts in multiple species across a broad collection of behaviors. Remarkably, even when only a small number of frames are labeled (~200), the algorithm achieves excellent tracking performance on test frames that is comparable to human accuracy.
Despite heavy research, blue perovskite nanocrystal LEDs have struggled to match the high efficiencies of their red and green cousins, particularly at wavelengths blue enough to meet the NTSC blue standard. One of the most critical problems is the low photoluminescence yield of the nanocrystals in thin films. Recently, manganese doping has been shown to increase the photoluminescence yield of the perovskite even as a decay pathway to a long-lived emissive state on the manganese ion is introduced. Here, we employ a two-step synthetic approach to carefully tune the manganese doping to increase the blue photoluminescence while preventing significant manganese emission, allowing for blue perovskite LEDs with quantum efficiencies over 2% that meet the NTSC standard. Manganese doping increases the photoluminescence yield and lifetime, reduces trap states, and makes the dots more monodisperse, reducing the emission bandwidth. Finally, we utilize perovskite nanocrystal downconverters to build an all-perovskite white LED.
Light‐emitting diodes utilizing perovskite nanocrystals have generated strong interest in the past several years, with green and red devices showing high efficiencies. Blue devices, however, have lagged significantly behind. Here, it is shown that the device architecture plays a key role in this lag and that NiOx, a transport layer in one of the highest efficiency devices to date, causes a significant reduction in perovskite luminescence lifetime. An alternate transport layer structure which maintains robust nanocrystal emission is proposed. Devices with this architecture show external quantum efficiencies of 0.50% at 469 nm, seven times higher than state‐of‐the‐art devices at that wavelength. Finally, it is demonstrated that this architecture enables efficient devices across the entire blue‐green portion of the spectrum. The improvements demonstrated here open the door to efficient blue perovskite light‐emitting diodes.
Single-atom catalysts have emerged as an exciting paradigm with intriguing properties different from their nanocrystal counterparts. Here we report Ni single atoms dispersed into graphene nanosheets, without Ni nanoparticles involved, as active sites for the electrocatalytic CO2 reduction reaction (CO2RR) to CO. While Ni metal catalyzes the hydrogen evolution reaction (HER) exclusively under CO2RR conditions, Ni single atomic sites present a high CO selectivity of 95% under an overpotential of 550 mV in water, and an excellent stability over 20 hours’ continuous electrolysis. The current density can be scaled up to more than 50 mA cm−2 with a CO evolution turnover frequency of 2.1 × 105 h−1 while maintaining 97% CO selectivity using an anion membrane electrode assembly. Different Ni sites in graphene vacancies, with or without neighboring N coordination, were identified by in situ X-ray absorption spectroscopy and density functional theory calculations. Theoretical analysis of Ni and Co sites suggests completely different reaction pathways towards the CO2RR or HER, in agreement with experimental observations.
Electrocatalytic CO2 reduction to higher-value hydrocarbons beyond C1products is desirable for applications in energy storage, transportation and the chemical industry. Cu catalysts have shown the potential to catalyse C–C coupling for C2+ products, but still suffer from low selectivity in water. Here, we use density functional theory to determine the energetics of the initial C–C coupling steps on different Cu facets in CO2 reduction, and suggest that the Cu(100) and stepped (211) facets favour C2+ product formation over Cu(111). To demonstrate this, we report the tuning of facet exposure on Cu foil through the metal ion battery cycling method. Compared with the polished Cu foil, our 100-cycled Cu nanocube catalyst with exposed (100) facets presents a sixfold improvement in C2+ to C1 product ratio, with a highest C2+Faradaic efficiency of over 60% and H2 below 20%, and a corresponding C2+ current of more than 40 mA cm–2.
The surface electronic structures of catalysts need to be carefully engineered in CO2 reduction reaction (CO2RR), where the hydrogen evolution side reaction usually takes over under a significant overpotential, and thus dramatically lowers the reaction selectivity. Surface oxides can play a critical role in tuning the surface oxidation state of metal catalysts for a proper binding with CO2RR reaction intermediates, which may significantly improve the catalytic activity and selectivity. Here, we demonstrate the importance of surface-bonded oxygen on silver nanoparticles in altering the reaction pathways and improving the CO2RR performances. A comparative investigation on air-annealed Ag (Air-Ag) catalyst with or without the post-treatment of H2 thermal annealing (H2-Ag) was performed. In Air-Ag, the subsurface chemically bonded O species (O–Agδ+) was identified by angle resolved X-ray photoelectron spectroscopy and X-ray absorption spectroscopy techniques, and contributed to the improved CO selectivity rather than H2in CO2RR electrolysis. As a result, though the maximal CO Faradaic efficiency of H2-Ag is at ∼30%, the Air-Ag catalyst presented a high CO selectivity of more than 90% under a current density of ∼21 mA/cm2.