High saturation magnetization, hysteresis-less long linear response range, and resistance to devicefabrication conditions are figures of merit for magnetic materials in science and technology.Despite advances in materials research, many high-saturating micro- and nanomagnetic materialsare hysteresis-prone, have short linear ranges, and are sensitive to oxidation, resulting in deviceinefficiencies in high-frequency electronics and unpredictable responses in magnetic sensing applications.Holmium oxide is a promising material because of its high magnetic susceptibility,but synthetic options are limited, with low-temperature magnetism incompletely characterized.Here, we present physical vapor deposition synthesis and material characterization of polycrystallineholmium oxide thin films. The product has saturation magnetization exceeding 2 Tesla,linear range (μ0H) also exceeding 2 Tesla, zero magnetic remanence and coercivity, and resistanceto harsh processing conditions including oxygen plasma and concentrated hydrofluoricacid etching, making these thin films ideal for fabricating next-generation nanoscale magneticdevices in advanced scientific and engineering applications.
High saturation magnetization and hysteresis-less magnetic responses are desirable for nanoparticles in scientific and technological applications. Rare-earth oxides are potentially promising materials because of their paramagnetism and high magnetic susceptibility in the bulk, but the magnetic properties of their nanoparticles remain incompletely characterized. Here, we present full M–H loops for commercial RE2O3 nanoparticles (RE = Er, Gd, Dy, Ho) with radii from 10–25 nm at room temperature and 4 K. The magnetic responses are consistent with two distinct populations of atoms, one displaying the ideal Re3+ magnetic moment and the other displaying a sub-ideal magnetic moment. If all sub-ideal ions are taken to be on the surface, the data are consistent with ≈2−10" id="MathJax-Element-1-Frame" role="presentation" style="border-bottom-color:currentColor;border-bottom-style:none;border-bottom-width:0px;border-image-outset:0;border-image-repeat:stretch;border-image-slice:100%;border-image-source:none;border-image-width:1;border-left-color:currentColor;border-left-style:none;border-left-width:0px;border-right-color:currentColor;border-right-style:none;border-right-width:0px;border-top-color:currentColor;border-top-style:none;border-top-width:0px;direction:ltr;display:inline;float:none;100%;none;font-style:normal;font-weight:normal;letter-spacing:normal;line-height:normal;margin-bottom:0px;margin-left:0px;margin-right:0px;margin-top:0px;max-height:none;max-width:none;min-height:0px;min-width:0px;overflow-wrap:normal;padding-bottom:0px;padding-left:0px;padding-right:2px;padding-top:0px;position:relative;text-align:left;text-indent:0px;text-transform:none;white-space:nowrap;word-spacing:normal;" tabindex="0">2−10≈2−10 nm surface layers of reduced magnetization. The magnetization of the rare-earth oxide nanoparticles at low temperatures (1.3–1.9 T) exceeds that of the best iron-based nanoparticles, making rare-earth oxides candidates for use in next-generation cryogenic magnetic devices that demand a combination of hysteresis-less response and high magnetization.
This work was supported by a Rowland Fellowship to Y.T. K.T. acknowledges support from the Rowland Institute and the Harvard Office of Undergraduate Research and Fellowships. The authors would like to thank Shaw Huang for assistance with SQUID and all group members for helpful discussions. SEM sample characterization studies were carried out at the Center for Nanoscale Systems (CNS) at Harvard University.
The characteristic red color of many natural tourmalines is due to the presence of Mn(III) cations substituting for aluminum and lithium. These sites originate as Mn(II) and are oxidized by natural γ-irradiation over geologic time as they sit in the Earth’s crust. Presented here is a thorough analysis of the spin-allowed and spin-forbidden transitions which give rise to the color of these gemstones. Ligand field analysis, supplemented by time-dependent density functional theory, was used to correct the historical assignments of the symmetry-allowed transitions in the polarized UV–visible absorption spectrum. Heat-induced reduction of the oxidized manganese sites provided a probe of the relationship between the spin-allowed and spin-forbidden bands. Notably, the intensity of the spin-forbidden transition was highly dependent on the neighboring ions in the Y-site. Simulations and modeling showed that increased intensity was observed only when two Mn(III) ions occupied adjacent substitutions in the Y-site via a proposed exchange-coupling mechanism.
Elucidating elementary mechanisms that underlie bacterial diversity is central to ecology1,2 and microbiome research3. Bacteria are known to coexist by metabolic specialization4, cooperation5 and cyclic warfare6,7,8. Many species are also motile9, which is studied in terms of mechanism10,11, benefit12,13, strategy14,15, evolution16,17 and ecology18,19. Indeed, bacteria often compete for nutrient patches that become available periodically or by random disturbances2,20,21. However, the role of bacterial motility in coexistence remains unexplored experimentally. Here we show that—for mixed bacterial populations that colonize nutrient patches—either population outcompetes the other when low in relative abundance. This inversion of the competitive hierarchy is caused by active segregation and spatial exclusion within the patch: a small fast-moving population can outcompete a large fast-growing population by impeding its migration into the patch, while a small fast-growing population can outcompete a large fast-moving population by expelling it from the initial contact area. The resulting spatial segregation is lost for weak growth–migration trade-offs and a lack of virgin space, but is robust to population ratio, density and chemotactic ability, and is observed in both laboratory and wild strains. These findings show that motility differences and their trade-offs with growth are sufficient to promote diversity, and suggest previously undescribed roles for motility in niche formation and collective expulsion–containment strategies beyond individual search and survival.
The gut is a first point of contact with ingested xeno- biotics, where chemicals are metabolized directly by the host or microbiota. Atrazine is a widely used pesticide, but the role of the microbiome metabolism of this xenobiotic and the impact on host responses is unclear. We exposed successive generations of the wasp Nasonia vitripennis to subtoxic levels of atrazine and observed changes in the structure and function of the gut microbiome that conveyed atra- zine resistance. This microbiome-mediated resis- tance was maternally inherited and increased over successive generations, while also heightening the rate of host genome selection. The rare gut bacteria Serratia marcescens and Pseudomonas protegens contributed to atrazine metabolism. Both of these bacteria contain genes that are linked to atrazine degradation and were sufficient to confer resistance in experimental wasp populations. Thus, pesticide exposure causes functional, inherited changes in the microbiome that should be considered when as- sessing xenobiotic exposure and as potential coun- termeasures to toxicity.
Recent advances in computer vision have made accurate, fast and robust measurement of animal behavior a reality. In the past years powerful tools specifically designed to aid the measurement of behavior have come to fruition. Here we discuss how capturing the postures of animals — pose estimation - has been rapidly advancing with new deep learning methods. While challenges still remain, we envision that the fast-paced development of new deep learning tools will rapidly change the landscape of realizable real-world neuroscience.
Iron-doped nickel layered double hydroxides (LDHs) are among the most active heterogeneous water oxidation catalysts. Due to inter-spin interactions, however, the high density of magnetic centers results in line-broadening in magnetic resonance spectra. As a result, gaining atomic-level insight into the catalytic mechanism via electron paramagnetic resonance (EPR) is not generally possible. To circumvent spin-spin broadening, iron and nickel atoms were doped into non-magnetic [ZnAl]-LDH materials and the coordination environments of the isolated Fe(III) and Ni(II) sites were characterized. Multifrequency EPR spectroscopy identified two distinct Fe(III) sites (S = 5/2) in [Fe:ZnAl]-LDH. Changes in zero field splitting (ZFS) were induced by dehydration of the material, revealing that one of the Fe(III) sites is solvent-exposed (i.e. at an edge, corner, or defect site). These solvent-exposed sites feature an axial ZFS of 0.21 cm-1 when hydrated. The ZFS increases dramatically upon dehydration (to -1.5 cm-1), owing to lower symmetry and a decrease in the coordination number of iron. The ZFS of the other (“inert”) Fe(III) site maintains an axial ZFS of 0.19-0.20 cm-1 under both hydrated and dehydrated conditions. We observed a similar effect in [Ni:ZnAl]-LDH materials; notably, Ni(II) (S = 1) atoms displayed a single, small ZFS (±0.30 cm-1) in hydrated material, whereas two distinct Ni(II) ZFS values (±0.30 and ±1.1 cm-1) were observed in the dehydrated samples. Although the magnetically-dilute materials were not active catalysts, the identification of model sites in which the coordination environments of iron and nickel were particularly labile (e.g. by simple vacuum drying) is an important step towards identifying sites in which the coordination number may drop spontaneously in water, a probable mechanism of water oxidation in functional materials.
The brain has persistent internal states that can modulate every aspect of an animal’s mental experience1,2,3,4. In complex tasks such as foraging, the internal state is dynamic5,6,7,8. Caenorhabditis elegans alternate between local search and global dispersal5. Rodents and primates exhibit trade-offs between exploitation and exploration6,7. However, fundamental questions remain about how persistent states are maintained in the brain, which upstream networks drive state transitions and how state-encoding neurons exert neuromodulatory effects on sensory perception and decision-making to govern appropriate behaviour. Here, using tracking microscopy to monitor whole-brain neuronal activity at cellular resolution in freely moving zebrafish larvae9, we show that zebrafish spontaneously alternate between two persistent internal states during foraging for live prey (Paramecia). In the exploitation state, the animal inhibits locomotion and promotes hunting, generating small, localized trajectories. In the exploration state, the animal promotes locomotion and suppresses hunting, generating long-ranging trajectories that enhance spatial dispersion. We uncover a dorsal raphe subpopulation with persistent activity that robustly encodes the exploitation state. The exploitation-state-encoding neurons, together with a multimodal trigger network that is associated with state transitions, form a stochastically activated nonlinear dynamical system. The activity of this oscillatory network correlates with a global retuning of sensorimotor transformations during foraging that leads to marked changes in both the motivation to hunt for prey and the accuracy of motor sequences during hunting. This work reveals an important hidden variable that shapes the temporal structure of motivation and decision-making.
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.