How did COVID-19 mortality rates during 2020 in the United States vary by race and ethnicity in combination with educational attainment?In this cross-sectional study of 219.1 million adults aged 25 years or older, most racial and ethnic minority populations had higher age-adjusted mortality rates than non-Hispanic White populations, including when comparing within levels of educational attainment. If all racial and ethnic populations had experienced the same mortality rates as college-educated non-Hispanic White populations, 71% fewer deaths among racial and ethnic minority populations would have occurred.This study suggests that public health research and practice should attend to the ways in which populations that share socioeconomic characteristics may still experience racial and ethnic inequity in COVID-19 mortality rates. Racial and ethnic inequities in COVID-19 mortality have been well documented, but little prior research has assessed the combined roles of race and ethnicity and educational attainment.To measure inequality in COVID-19 mortality jointly by race and ethnicity and educational attainment.This cross-sectional study analyzed data on COVID-19 mortality from the 50 US states and the District of Columbia for the full calendar year 2020. It included all persons in the United States aged 25 years or older and analyzed them in subgroups jointly stratified by age, sex, race and ethnicity, and educational attainment.Population-based cumulative mortality rates attributed to COVID-19.FAmong 219.1 million adults aged 25 years or older , 376 125 COVID-19 deaths were reported. Age-adjusted cumulative mortality rates per 100 000 ranged from 54.4  among Asian women with some college to 699.0  among Native Hawaiian and Other Pacific Islander men with a high school degree or less. Racial and ethnic inequalities in COVID-19 mortality rates remained when comparing within educational attainment categories . If all groups had experienced the same mortality rates as college-educated non-Hispanic White individuals, there would have been 48% fewer COVID-19 deaths among adults aged 25 years or older overall, including 71% fewer deaths among racial and ethnic minority populations and 89% fewer deaths among racial and ethnic minority populations aged 25 to 64 years.Public health research and practice should attend to the ways in which populations that share socioeconomic characteristics may still experience racial and ethnic inequity in the distribution of risk factors for SARS-CoV-2 exposure and infection fatality rates . This study suggests that a majority of deaths among racial and ethnic minority populations could have been averted had all groups experienced the same mortality rate as college-educated non-Hispanic White individuals, thus highlighting the importance of eliminating joint racial-socioeconomic health inequities. This cross-sectional study measures inequality in US COVID-19 mortality jointly by race and ethnicity and educational attainment. One US-wide study found roughly equal cumulative mortality rates by race and ethnicity within educational attainment categories, but the data source did not allow for age standardization and, as the authors noted, the results were likely to underestimate racial and ethnic inequities because US White populations have distributions that skew older.2 That study, along with another analysis of California’s Latinx population,4 found that persons in the lowest socioeconomic position experienced the highest COVID-19 mortality rates within racial and ethnic groups.Numerous studies have documented racial and ethnic inequities in COVID-19 mortality rates in the United States.5 We assessed 3 hypotheses with our study: (1) racial and ethnic minority populations would experience higher age-adjusted COVID-19 mortality rates than the non-Hispanic White population; (2) within racial and ethnic groups, age-adjusted COVID-19 mortality rates would be highest among those with the lowest educational attainment; and (3) racial and ethnic inequalities in COVID-19 mortality rates would remain when comparing within levels of educational attainment.Our study uses a recently available public data set on COVID-19 mortality, which permits subgroup analysis by race and ethnicity, educational attainment, age, and sex, and therefore allows for a more complete examination of racialized socioeconomic inequities than previous studies. Our research is informed by prior literature that has illustrated how racialized differences in health are not reducible to inequalities in educational attainment, and involve multiple other pathways .6 This study analyzed solely deidentified, public-use data and was therefore exempt from institutional review board approval. This study followed the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) reporting guidelines for cross-sectional studies.7We conducted cross-sectional analyses of cumulative COVID-19 mortality rates for US population aged 25 years or older. Although data on younger individuals were available, we excluded them for 2 reasons. First, there were a small number of deaths among those aged 24 years or younger . Second, educational attainment may not be a valid indicator of socioeconomic position for this group, as many are too young to have been able to complete schooling. Reflecting this concern, the US Census Bureau’s own analyses of education data typically exclude those younger than 25 years of age.We quantified racial and ethnic inequity as MRRs, both overall and within educational attainment categories. The analyses were conducted separately for each age-sex subgroup. Finally, we calculated the number of COVID-19 deaths that would have occurred if everyone had experienced the same mortality rate as college-educated non-Hispanic White individuals  of the same age and sex. All analyses were based on publicly available US Census and US mortality data.8 The data set is based on reporting by the 50 states and the District of Columbia for the full calendar year 2020 and reported to CDC by February 24, 2021. This data set represents the most recent available data. The file includes counts of deaths due to COVID-19 stratified by race and ethnicity, age group, sex, and educational attainment  certification or less; some college, which includes an associate’s degree; or a bachelor’s degree or more). We treated educational attainment as a measure of socioeconomic position; other measures such as income, wealth, and occupation were not provided in the data set and are not as readily available on death certificate–based mortality reporting systems.9 The CDC notes that the data set is provisional because there may be undercounts in later weeks due to reporting lags—however, the data were current as of 8 weeks past December 31, 2020, so the effect of lags should be minimal.We analyzed open access COVID-19 mortality data provided by the US Centers for Disease Control and Prevention (CDC).10 We tabulated these data for use as population denominators for all cumulative mortality rate calculations.Sociodemographic data are reported on death certificates, typically by the funeral director. Sex was reported as “male” and “female,” with no data on whether decedents were nonbinary or transgender. Race and ethnicity were reported as “Hispanic” , as well as non-Hispanic racial groups: non-Hispanic Black, non-Hispanic White, non-Hispanic Asian, non-Hispanic American Indian or Alaska Native, and non-Hispanic Native Hawaiian and Other Pacific Islander. We treated the small proportion of decedents (<0.3%) whose race and ethnicity was categorized as “non-Hispanic more than one race” as missing and excluded them from subsequent analyses. We additionally obtained census microdata from the US Census American Community Survey for the period from 2017 to 2019.dstdize command in Stata, version 16 (StataCorp LLC) based on the CDC year 2000 standard population.11 To compare racial and ethnic inequality within educational attainment categories, we calculated age-adjusted cumulative MRRs comparing COVID-19 death rates for each racial and ethnic group with non-Hispanic White individuals of the same age group, sex, and education group.We calculated cumulative mortality rates for COVID-19 by race and ethnicity, sex, and educational attainment for the population aged 25 years or older. We conducted analyses for the entire population aged 25 years or older, as well as for the younger (25-64 years) and older (≥65 years) populations. When calculating rates for the overall, younger, and older populations, we applied direct standardization using the In addition, we simulated a counterfactual scenario to estimate the number of deaths that would have occurred had each population group experienced the same cumulative mortality rate as the group that, theoretically, has the most racialized socioeconomic privilege: college-educated non-Hispanic White individuals. We did this by multiplying each stratum’s populations size by the mortality rate observed among college-educated non-Hispanic White individuals of the same age and sex.13 However, the 95% CIs represent uncertainty owing to sampling, but the mortality data represent a finite population , which are not subject to sampling error. Confidence intervals for MRRs that include the null value of 1.0 should therefore not be interpreted as “no statistically significant difference.” We include uncertainty estimates by convention, but focus our interpretations on the point estimates of rates and MRRs.All 95% CIs for cumulative mortality rates and MRRs reported below assume that mortality rates follow a Poisson distribution and are calculated using standard formulas for directly standardized rates and rate ratios.Among 219.1 million adults aged 25 years or older , 376 125 individuals ages 25 years or older died of COVID-19 during the year 2020 . Among tAge-adjusted cumulative mortality rates per 100 000 population ranged from 54.4  among Asian women with some college to 699.0  among Native Hawaiian and Other Pacific Islander men with a high school degree or less. Within race-gender groups, the highest age-adjusted cumulative mortality rates were consistently experienced by those with the lowest educational attainment . For theNearly all racial and ethnic minority subgroups  experienced higher mortality (MRR >1.0) than their non-Hispanic White counterparts . The onlRacial and ethnic inequality in mortality for the overall population remained and was slightly attenuated, on average, when comparing within education categories. For the results presented in In a counterfactual scenario in which all people experienced the same mortality rates as college-educated non-Hispanic White individuals of the same age and sex, the total number of deaths due to COVID-19 would have been 48% lower among adults aged 25 years or older, preventing 176 000 of the 364 000 deaths for which complete sociodemographic data were available. For all racial and ethnic minority individuals, the number of deaths due to COVID-19 would have been 71% lower, preventing 100 000 deaths out of 141 000. For racial and ethnic minority individuals aged 25 to 64 years, the number of deaths would have been 89% lower, preventing 40 000 deaths out of 45 000.Public-access data covering the entire United States have recently become available to examine inequities in COVID-19 mortality jointly by race and ethnicity and socioeconomic position. Our study of population-based data among adults aged 25 years or older identified inequities by educational attainment for the overall population and within racial and ethnic groups. Racial and ethnic minority populations typically experienced higher age-adjusted mortality rates than non-Hispanic White individuals and those with the lowest socioeconomic position (high school or GED completion or less) also died at the highest rates within racial and ethnic groups. The socioeconomic privilege afforded by higher educational attainment among racial and ethnic minority populations was often insufficient to overcome racial and ethnic inequality. For example, adjusting for age, college-educated non-Hispanic Black men had higher COVID-19 mortality rates than non-Hispanic White men who had completed high school or GED or less. Although a gradient in mortality rates by educational attainment was found within all population groups, the association of unequal educational attainment with racial and ethnic inequalities in COVID-19 deaths was likely modest.Racialized socioeconomic inequities played a substantial role in the overall death toll of the pandemic. Had all population groups experienced the mortality rates observed among the group assumed to be the most racially and socioeconomically privileged , the overall number of COVID-19 deaths in the US during 2020 would have been halved, and deaths among racial and ethnic minorities aged 25 to 64 years would have been reduced to about one-tenth of the observed number.15 With regard to COVID-19 specifically, census data show all racial and ethnic groups other than non-Hispanic White individuals are more likely to have risk factors for SARS-CoV-2 exposure  than non-Hispanic White individuals of the same educational attainment category  have nevertheless experienced the highest COVID-19 mortality rates, suggesting that similarities in COVID-19 outcomes are associated more with similar social conditions than population genetics.One of our key findings—that the magnitude of racial inequities was only slightly attenuated on average when stratifying by educational attainment categories—warrants further explanation and, ultimately, more exploration in future research. Racial and ethnic differences in educational attainment, and in socioeconomic position more broadly, capture only one mechanism through which structural racism is associated with health. Prior research shows that racial and ethnic differences in economic assets vary considerably even within education levels. For example, non-Hispanic Black college graduates in the US have less wealth and lower rates of home ownership when compared with non-Hispanic White college graduates and even when compared with non-Hispanic White indiviuals who have not graduated high school.9 may be particularly useful in analyses of COVID-19 outcomes, because class is closely tied to power—in this case, power to mitigate exposure to SARS-CoV-2.Different measures of socioeconomic position such as wealth or income may have yielded different patterns of racial inequality. In addition, accounting for social class, which, unlike socioeconomic position, is defined by one’s position in economic relationships ,21 It is unclear whether similar underestimation of mortality also applies to Native Hawaiian and Other Pacific Islander individuals, as they have not been disaggregated from non-Hispanic Asian individuals in prior research on misclassification of race and ethnicity in mortality data. In addition, COVID-19 is likely misclassified in mortality data, and the CDC estimates that the true number of COVID-19 deaths was 30% higher than reported for the period from March 2020 to May 2021,22 although whether misclassification varies by sociodemographic groups is unknown. As this was a cross-sectional study, we were also unable to assess whether the magnitude of inequalities changed over time. Finally, owing to limitations in the mortality data set, we were not able to assess potential mediators associated with racial and ethnic and socioeconomic inequalities, including geography, SARS-CoV-2 exposure, health care access, and comorbidities.This study has some limitations. The analyses in this study relied on race and ethnicity classification of decedents from death certificates, which prior research has demonstrated to substantially underestimate mortality for American Indian or Alaska Native populations.During the first year of the COVID-19 pandemic, which largely preceded the availability of vaccines, there were extremely high levels of racialized economic inequity in the distribution of COVID-19 mortality. Future research may investigate the specific pathways that produced these joint racial and ethnic and socioeconomic inequities, as well as whether the longstanding political disempowerment of populations among whom the virus was most lethal  was associated with policy responses to the pandemic. What is clear is that the mortality burden of these inequities is high. Future research can help inform interventions that yield equitable responses to both the ongoing burden of COVID-19 and potential future pandemics that spread similarly to SARS-CoV-2.


We will discuss the general concept of photodoping, the spectroscopic and theoretical tools to determine the charging process, together with unresolved open questions. We conclude the review by highlighting possible device architectures based on doped MO NCs that are expected to considerably impact the field of energy storage by combining in a unique way the conversion and storage of solar power and opening the path towards competitive and novel light-driven energy storage solutions.The growing demand for self-powered devices has led to the study of novel energy storage solutions that exploit green energies whilst ensuring self-sufficiency. In this context, doped metal oxide nanocrystals (MO NCs) are interesting nanosized candidates with the potential to unify solar energy conversion and storage into one set of materials. In this review, we aim to present recent and important developments of doped MO NCs for light-driven multi-charge accumulation  of doped metal oxide nanocrystals opens the way to innovative solutions for the direct conversion and storage of the solar energy.Light-driven multi-charge accumulation ( Power generation based on energy harvesting from clean renewable sources  requires their combination with an energy storage unit to overcome the fluctuations related to their intermittent availability.3 Light-powered energy storage solutions have become a promising direction.4 In such devices, often energy conversion and storage are carried out in stand-alone units each covering a separate functionality. The active materials involved pursue independent tasks: the process of solar energy absorption followed by charge separation is distinct from the charge accumulation.5 Loss mechanisms related to the coupling of the two subunits often limit the overall efficiency. Moreover, single (or even less) charge transfer reactions are involved.5The global energy crisis together with the growing demand for powered devices represents one of the main challenges of today's society.6 As a consequence, doped MO NCs have been studied as alternative solution-processable materials for many different applications over the past years.7–10 Particularly interesting is the opportunity to introduce multiple free charge carriers into the MO NC via light absorption. This light-induced doping process, in the following termed photodoping, offers a convenient way to in situ and non-destructively modulate carrier densities in NCs.11 The photo-induced n-type doping of NCs is accompanied by the capture of photogenerated holes with suitable hole quenchers. This results in the accumulation of extra electrons within the NCs, while charge compensating cations deposit on the NC surface.11 This method has been successfully applied to a number of semiconductor NCs. For example, photodoping was implemented to generate and manipulate electron spins in magnetic semiconductor NCs for investigation of carrier–dopant interactions.12 In Cd-based chalcogenide NCs, such as CdSe NCs, photodoping was used to increase the level of doping involving the occupation of quantized energy levels.11 Photochemically n-doped colloidal CdSe NCs were implemented for manipulating hot electron dynamics.13 However, Cd-chalcogenides possess shallow valence bands and consequently very reactive hole quenchers are required to perform efficient photodoping.11 Moreover, only a very limited number of electrons  are introduced with photodoping, owing to the discrete energy levels involved in the process.11 Notably, MO NCs are able to store multiple delocalized charges per NC after light absorption reaching tens14,15 to hundreds6,16 of extra conduction band electrons per NCs. The resulting nanoscale ‘supercapacitors’ display attractive capacitance values competing well with materials currently employed in state-of-the-art energy storage systems with the added value of being charged by light.17 Recently, this solution-based photodoping process, has been transferred to an all-solid-state platform.18 Despite these promising results, up to now in MO NCs only light-driven charge accumulation was demonstrated, while the fully-working device and the demonstration of light-driven energy storage remains an open task.Doped metal oxide nanocrystals (MO NCs) are potential candidates combining light conversion and storage in the same material.In this review, we introduce the light-driven charging of doped MO NCs by discussing the most important physical and chemical processes involved, the theoretical background, and open issues. We highlight scenarios envisaged to exploit the photodoping and multi-charge accumulation processes of this emergent material for energy storage solutions, taking a step towards the direct conversion of solar radiation into readily available electrical energy.20–1021 cm−3. The free carrier density is controlled by the level of doping, ultimately delivering a tool to continuously tune the optical properties in a broad range of wavelengths. Metal oxides NCs present a rich variety of synthesis methods ranging from solution based, such as solvothermal methods, to sol–gel methods, to microemulsion methods, to microwave-assisted methods, to solvothermal methods, to vapor state based and biological methods.19,20 Developments in non-aqueous sol–gel methods managed to overcome many drawbacks of aqueous systems by exploiting inert organic solvents21,22 and recently several doped MO NCs (such as ITO NCs) have been synthesized using a continuous growth approach with slow-injection of precursors, significantly improving the control over the main parameters of the synthesis.23,24 Doping at the stage of synthesis exploits several chemical strategies, including (1) intrinsic doping due to lattice point vacancies , (2) extrinsic interstitial doping (such as Cs doping in WO3 NCs), or (3) aliovalent substitutional doping with donor impurities (such as Sn for In2O3 or Fe dopants for ZnO).17,25–28 Post-synthetic doping and dynamic modulation of the carrier density relies on the introduction of extra charges in a capacitive charging process by applying either an electrochemical potential or by interaction with light beyond their bandgap.29,30 In the photodoping process .32–34 The participation of hole scavengers in the photodoping process is crucial. The hole capture process (<15 ps) is an order of magnitude faster than the competing Auger recombination (∼150 ps) of the photogenerated electron–hole pair, making the accumulation of multiple conduction-band electrons possible.16 Multicarrier Auger recombination rates increase with the number of excess charge carriers and compete kinetically with the hole scavenger's photo-oxidation. The equilibrium of these two processes ultimately results in an average maximum number of excess electrons that can be stored per NC. The photodoping process, thus, depends strongly on the nature of the hole scavenger and is not necessarily an intrinsic property of the NCs themselves.16 In fact, the average maximum number of stored charges per single NC varies significantly when different hole scavenger molecules, such as alcohols, organic acids, hydrocarbons, or even organometallic compounds are employed.16 Strong hole scavengers, e.g. Li[Et3BH] and K[Et3BH], lead to a significantly greater number of stored charges, with the drawback of making the photodoping process irreversible.35,36 The light-induced oxidation of hole scavengers at the surface of the MO nanoparticles, occurs either through direct oxidation or indirectly by reacting with interfacial OH radicals resulting from the trapping of the holes at –OH surface groups.14,16,36–38 The positively charged hole scavenger molecule is electrostatically attracted to the surface of the NC, guaranteeing the overall neutrality of the system. The identity of the charge-compensating counteraction induces systematic shifts in the Fermi level (EF), having important implications on the electronic properties of the NC.39 For example, in the case of ZnO nanocrystals, the charge storage equilibrium is mainly determined by the ethanol (EtOH) oxidation process, where two EtOH molecules accept extra holes from the NC, resulting in the formation of acetaldehyde (ZnO + ½CH3CH2OH → ZnO− + H+ + ½CH3CHO).14 The maximum storage in ZnO NCs is thermodynamically defined by the reverse process, in which acetaldehyde is hydrogenated, hindering the accumulation of additional electrons (ZnO− + H+ + ½CH3CHO → ZnO + ½CH3CH2OH). Acetaldehyde accumulation and proton-driven surface reactions induce Fermi level pinning, deforming the electronic structure of the nanocrystal.14Doped MO NCs display unique optical properties determined by their free carrier density in the range of 10 process , the abs14,32 The maximum number of extra electrons that can be introduced via photodoping is independent of the initial carrier density of the doped MO NCs. Several works reported that the final carrier density that can be reached through photodoping (when using the same hole scavenger) is a constant value.16,32 Consequently, the total amount of photo-accumulated charges displays a cubic growth radius law (R3) with the volume of the NC.16 Moreover, Coulomb interactions and surface reactions can also influence the photodoping processes.16,40Besides the hole scavenger, the photodoping process is controlled by several factors.14,15,41 Briefly, a precise concentration of titrant (molecular oxidants) is added to the suspension of photodoped nanocrystals. This triggers the oxidation reaction of the NC with the extra photoelectrons and the subsequent recovery of the initial conditions.32 Under galvanostatic conditions (no current flowing), the working electrode and the colloidal dispersion can be kept constantly at the same potential with the support of a potentiostat. In this case, it is possible to extract the Fermi level (EF) of the NCs and follow variations in its values by tracking changes in the half-cell potential.16,40 The analysis of the slope of the open-circuit potential  versus the added equivalents of titrant allows extracting the capacitance C, calculated as C = Δq/ΔV, where Δq is the change of the charge carriers, and ΔV is the corresponding change in electrochemical potential  operate on principles similar to those of conventional electrostatic capacitors. Instead of using two conductive materials separated by a dielectric layer to store energy, EDLCs store electrical charges accumulated at the interface between the surface of a conductor and an electrolytic solution, where the phenomenon of the double-layer effect occurs.46 This purely electrostatic effect stores electrical charges as in a conventional capacitor, forming a static electric field, which corresponds to the intensity of the applied voltage. The bilayer roughly acts as a dielectric layer in a conventional capacitor.47–49 The charge separation is around a few angstrom50 and implemented in the following equation for the double-layer capacitance (Cdl):39A, ε0, εr, and r referring to the sphere's surface area, the permittivity of free space, the dielectric constant of the solvent, and the nanocrystal radius, respectively. This equation is the extension of the classical electrostatic capacitor  by an additional term that accounts for the thickness t of the electrical double layer.39 Photodoping of MO NCs, however, involves charge transfer reactions across the solid–liquid interface, which resembles more closely to the phenomenon of pseudo-capacitance. The pseudo-capacitance stores electrical energy through reversible faradaic redox reactions on the surface, accompanied by an electron charge-transfer.51 Usually a very fast sequence of reversible redox, intercalation, or electrosorption processes accompany such faradaic charge transfer, while no chemical reaction occurs.51,52 Such processes however are still under investigation, indicating that the real phenomenon is still an open issue. A more detailed study is required to support properly the characterization of devices based on photodoping. Careful control over surface chemistry, including surface ligands, will allow determining key properties of charge transfer across the interface. This will provide a comprehensive understanding of both the thermodynamics of charge transfer at the nanoscale and the charge storage mechanism  in the nanocrystals.The accumulation of electrostatically attracted positive charges on the surface of the negatively photodoped MO NCs resembles an electric double-layer. This indicates that the charged NCs can be understood as soluble nano-sized supercapacitors.via photodoping are delocalized.12,34,53 Such charges add to the initial carrier density of the doped MO, together resulting in localized surface plasmon resonances (LSPRs)  and the free carrier density (ne), spectroscopic investigation allows monitoring the doping process.8,10,24,59–62 In fact, the process of photodoping in doped MO NCs can be tracked in real-time by following the spectral modification of the localized plasmonic resonance. Experiments on ITO NCs with increasing UV exposure time and anaerobic conditions show a blue shift of the LSPR peak, which is also accompanied by a significant increase of the plasmonic response . The necessary equivalents of titrants to turn the system to its initial conditions correspond to the number of extra electrons stored in the system  the nanoparticle Drude-like complex dielectric permittivity ε(ω) = ε∞ − ωP2/(ω2 + iωΓ). From ωLSPR is provided by the so-called Fröhlich condition, Re(ε(ω)) = −2εH, leading to: is the bulk plasma frequency with ne the free carrier density, m* is the effective electron mass and Γ is a damping parameter.Several models established for conventional plasmonic materials have been adapted to describe the optical response of MO NCs.59,64–66 Such models were adapted to track the optical changes occurring by introducing extra electrons in an electrochemical charging process. Here, the core represents the doped MO, while the shell describes a carrier depleted MO.32,59,65,66 The depletion of carriers close to the surface is a result of surface states, which are located energetically below the Fermi level and the related phenomenon of Fermi level pinning. This induces an upward bending of the conduction and valence band and results in the creation of a layer with heavily suppressed electron density extending over several nanometers into the material , power density (speed of energy transfer per unit volume or mass), response time, lifetime in years and cycles, and self-discharge.72,75,78 Most common types of batteries include lithium-ion (Li-ion), sodium–sulfur (NaS), nickel–cadmium (NiCd), lead-acid (Pb-acid), lead–carbon batteries, and zebra (Na-NiCl2) and flow batteries; while capacitors can be classified as electrostatic capacitors, electrolytic capacitors and supercapacitors. Among these three types, supercapacitors have the highest capacity per unit volume due to the porous structure of the electrode.78,79In this context, different energy storage technologies can be described and compared from a technical perspective.3 and 33 μF cm−2, for volumetric and areal capacitances respectively,17 rivalling the performance of commercially available supercapacitors.6,29–33 In this example, the Fe-doping introduces extra acceptor levels with energy just below the conduction band. After photodoping, charges are stored in these highly localized orbitals and still easily removed as a result of the equilibrium condition between the Fe levels and the conduction band of the MO NCs.17 The number of extra charges increases significantly due to Fe doping, proving that doping control is a promising and viable strategy for the enhancement of the storage capacity of MO NCs and represents the first indication for the potential of optimized doped MO NCs as light-driven supercapacitor materials.17 Due to the novelty of this approach there are, up to date, only a limited number of proof-of-concept supercapacitors based on MO NCs and, hence, we foresee important breakthroughs through the development of optimized devices in the incoming years.In a notable example of doped MO NCs, Fe doped ZnO NCs were described as soluble supercapacitors, with capacitance values as high as 233 F cm5 Research carried out over the past decade has shown that NCs, in general, present many advantages in energy storage in terms of high packing density, high surface-to-volume ratio, and short diffusion pathways, providing opportunities for simultaneously achieving high energy and power density.80 This versatility of NCs combined with the benefit of light-driven charge accumulation can open avenues to novel renewable energy storage designs.To reach such goals, the light-induced charge accumulation process now requests its transformation into high-performance and long-lasting solar energy storage solutions.81,82 In the state-of-the-art supercapacitor architectures, the predominant trend is to implement micro-supercapacitors with three-dimensional architecture.3 The latter essentially involves anodes and cathodes with exposed surfaces in three dimensions rather than the flat surface of conventional thin films, exploiting many nanostructured morphologies, such as nanocrystals, nanowires, nanotubes, nanosheets, and nanowalls.3 Such principles can be directly translated to MO NC-based electrodes to enhance specific capacitances after light absorption. Additional requirements to the employed MO nanostructures are related to high ionic adsorption capacity at the solid–liquid interface, fast and reversible surface redox reactions, and fast charge transfer as well as high conductivity, highlighting the requirement for further dedicated material design.3A promising route to exploit photodoping for solar energy storage is by developing light-driven electrodes . An impo, it is clear that the theoretical specific capacity (Q) directly relates to the number of electrons N involved in the reaction, the Faraday constant (F), and low molecular weight (M). Such multi-charge transfer reactions are considered key tools for achieving high energy density in batteries. This highlights MO NCs with multiple charge transfer capability as high energy density components in the next-generation of energy storage devices. Current multi-charge transfer components in energy storage suffer stability issues83 due to the formation of highly reactive species during the charge transfer.84 Superior stability through the delocalization of the stored charges64 may offer new effective solutions in particular when implemented in devices in remote places, which require technologies with a long service-life.A notable advantage of photodoped MO NCs lies in the opportunity to exploit multi-charge transfer steps. From 80 A visionary route is the development of solar rechargeable flow-cell systems by coupling the light-driven MO nano-inks to well-established device compartments already used in flow batteries.85 Systems, in which the charge-bearing electrolyte is spatially separated from the electrode, as in flow cell devices, deliver benefits to individually tune the energy and power density of the system, highlighting all together the benefit of implementing MO NCs in novel energy storage solutions . Energy and power densities in the range of μJ cm−2 and μW cm−2 are foreseen.18 To complete the fully functional, light-driven nano-capacitor device however fully functional charge extraction electrodes are required  were employed as all-solid-state hole-acceptors, accepting permanently multiple photo-generated holes from ITO NCs.18 While this serves only as a first estimate, it raises important questions regarding the expected properties of proper hole collectors. The band offset will have an important impact on the output potential in the solar-energy storage device for a given MO-hole collector pair and their band alignment represents a key requirement to enable efficient light-driven charge separation (In this example, the hypothesized cell voltage was derived from the energetic difference between the conduction band minimum and the valence band maximum of the electron and hole acceptor, respectively, resulting in an expected cell voltage of around ∼1.2 V.86,87 These have a significantly lower bandgap in the visible/near-infrared regimes. Photodoping of such NCs would represent an important step towards storing a larger potential of solar energy. Also the participation of an active hole collector in light absorption and electron donation would beneficially contribute to the overall efficiency of the devices. Designed in an appropriate way this might enable the extension of light absorption to the red spectral range. Therefore, additional materials research must focus on the development of appropriate hole collectors with multiple charge storage capability. The first examples highlighted that 2D monolayers are an interesting platform,18 while the full library of available materials still requires thorough investigation. This displays the importance of materials’ research in the study of MO NCs for light-driven energy storage. Another key point is the implementation of environmentally benign and non-toxic elements. In fact, the study of photodoping was so far limited to few metal oxides. The exploitation of the richness of chemical synthesis techniques will expand the list of materials to low-cost, environmentally friendly, and abundant materials.For competitive solar conversion efficiency, the absorption of the involved materials needs to be extended and red-shifted from predominantly UV to the visible part of the spectrum. Recent works described the capacitive properties of InN NCs.via photodoping could be exploited to design transistors in which the gate input is replaced with light, or even developing all-optical-input.88,89 In fact, in a recent work MO NCs were employed as all-optical light-driven charge injection tools to inject multiple electrons into 2D TMDs, in analogy to 2D material gating.89Apart from energy-related applications, doped MO NCs appear interesting as active elements for other light-driven optoelectronic nanodevices as well. The possibility to modulate the carrier density of doped semiconductors This together displays that MO NCs photodoping opens horizons for novel application spaces as active elements in self-powered nano-electronics. Moreover, light-driven MO NCs in proper device architectures display a promising and viable strategy for novel solar-powered energy storage units. With the focused research from the materials to the devices, MO NCs have the potential to contribute to a future sustainable and zero-emission energy landscape by exploiting fundamentally new concepts of solar energy conversion and storage.There are no conflicts to declare.


Crystal-inspired approach is found to be highly successful in designing extraordinarily damage-tolerant architected materials. i.e. meta-crystals, necessitating in-depth fundamental studies to reveal the underlying mechanisms responsible for the strengthening in meta-crystals. Such understanding will enable greater confidence to control not only strength, but also spatial local deformation. In this study, the mechanisms underlying shear band activities were investigated and discussed to provide a solid basis for predicting and controlling the local deformation behaviour in meta-crystals. The boundary strengthening in polycrystal-like meta-crystals was found to relate to the interaction between shear bands and polygrain-like boundaries. More importantly, the boundary type and coherency were found to be influential as they govern the transmission of shear bands across meta-grains boundaries. The obtained insights in this study provide crucial knowledge in developing high strength architected materials with great capacity in controlling and programming the mechanical strength and damage path. Polycrystal-inspired architected materials are found to be high strength and damage tolerant. Here, the authors conduct in-depth work to unravel the mechanism responsible for the hardening phenomenon, in particular the role of polygrain-like boundary in the post-yield shear band activities. Lattice materials formed by a periodic arrangement of unit cells consisting of a regular network of struts are one particular type of architected materials6. Extensive and in-depth works have been carried out with a focus on the elastic, plastic yield and toughness properties of singly orientated lattice materials made of different base materials8, various unit cells types, such as iso-truss9, octet-truss11, body-centred cubic15, gyroid16 and auxetic17, and fabrication techniques that enable extraordinary properties of hierarchical lattice materials21. One of the major issues for singly orientated lattices is the undesirable severe drops in strength after plastic yield due to the formation of dominant and fast propagating shear bands throughout the whole structure, in particular for stretching-dominated lattice materials made of elastoplastic materials or containing fabrication defects26. As a consequence, the strength and energy absorption capacity of lattice materials is substantially reduced, limiting the applicability of the materials for structural applications. The formation of the shear band is inherently related to the plastic yield, bending or buckling of constituent struts27. The uniform periodic arrangement of struts throughout the whole structure causes a consequence that once a strut collapses, the other struts of a given orientation collapse in the same way, causing the fast propagation of damage and substantial loss in strength28.Architected materials built on ordered arrangements of internal structures are lightweight and excellent at bearing load and absorbing impact energy, enabling their wide applications in the fields of aerospace, automobiles, medical devices, packaging and infrastructures29. The introduction of polycrystalline-inspired features to break the long-range periodicity in a singly orientated lattice into multiple domains named meta-grains of different orientations inhibits the fast propagation of the shear band and prevents catastrophic failure. The study demonstrates that the strength and stability of architected materials can be dramatically increased thanks to the significantly shortening and impeding effects of meta-grain boundaries on the dominant shear band. Moreover, the previous study clearly showed that the yield strength of meta-crystals increases with a reduction in the size of meta-grains, a similarity to the well-known Hall–Petch relationship found for polycrystalline metals34. Although the strengthening effects via reducing meta-grain size was clearly presented29, key mechanisms underlying the observed strengthening were not discussed. An in-depth understanding of such mechanisms is crucial in controlling and programming not only the strength but also the local spatial deformation of meta-crystals. To unravel the underlying mechanisms, it is necessary to study the shear-band activities and the interaction between the shear bands and the boundaries between meta-grains. The study of shear bands also helps to better understand the anisotropy of lattice materials, and to answer first two key remained questions: why were the shear band formed in the 29? Are there other systems that might be active? In addition, the study of the interactions between meta-grain boundaries and shear bands helps to answer other key questions: how is the boundary between meta-grains able to stop a shear band? Similar to the grain boundary strengthening in polycrystals36, do the characteristics, such as coherency, of boundaries affect the stopping effect? Understanding of the underlying mechanisms and answers to the questions will provide the designers more confidence and a sound basis to fully utilise the meta-crystal approach to develop lightweight and high-performing architected materials with controllable properties.A transformative approach of designing new both high strength and extraordinarily damage-tolerant architected materials, coined meta-crystals, was recently proposed to successfully translate the metallurgical mechanisms, in particular the grain boundary hardening, found in polycrystalline materials to eliminate the post-yield collapse and significantly strengthen lattice materials by designing polygrain-like structuresIn this study, we studied the mechanisms underlying the shear-band formation and identified active shear-band systems in a lattice structure of face-centred cubic (FCC). The buckling analyses were done to reveal the origin of shear-band formation, to explain and predict the shearing activities in FCC meta-crystals. Subsequently, meta-crystals mimicking polycrystals containing numbers of meta-grains ranging from 1 to 64 were designed and tested to study the shear-band propagation and interactions with meta-grain boundaries. The role of meta-grain boundaries and its coherency on shearing activities, yield strength and hardening were revealed and discussed.Z axis . The three meta-crystals were subsequently named FCC30, FCC45 and FCC60  regarding the loading directions parallel to global C60 Fig. . Figure ure Fig. . Similar27. In this study, it was found that buckling was responsible for the strut collapse in FCC meta-crystals. To obtain in-depth understanding of the activation of these shearing systems in the meta-crystals, critical force and stress for buckling of individual struts  and defects from the fabrication of unit cellsTo reveal the effect of meta-grain boundary, we designed a meta-crystal containing two meta-grains, i.e. bi-meta-crystal, that were twinned FCC45 with the twin boundary highlighted in Supplementary Fig. 4. Figure als Fig. . Positiv45, all the presented polygrain-like meta-crystals were fabricated using the same build direction which was along the global Z direction (“Methods”). Figure 29. Note that the stress–strain curves in this study include additional data points and were normalised by their relative densities to help minimising the effect induced by the relative density. In addition, it was reported that a relationship between the nominal yield strength 29, Eq. . In addition, to minimise the effect induced by dependence on build orientation in the fabrication processmentclass2pt{minimmentclass2pt{minim1/2 Fig. . The \do28. By contrast, the meta-crystal approach achieves both high yield strength, stable flow stress and significant hardening.The reduction in the size of meta-grains also improved post-yield strength and increasingly stabilised the flow stress of meta-crystals in agreement with Fig. ion Fig. . Such in6. According to Maxwell’s criteria46, the used FCC unit cell is a stretching-dominated lattice. It theoretically predicts that relative yield strength should scale linearly with relative density with the pre-factor coefficient constant varying from 0.3 to 0.547. The scaling relationship between relative density and relative yield strength , group 2  and group 3 , where groups 1 and 2 were of the tilt type and group 3 was of the twist type . Specifically, the enhanced stability and strength of meta-crystals during plastic deformation is mainly due to the strength of meta-grain boundary that is associated with the boundary coherency. Similar to the role of coherency at grain boundaries in polycrystalline alloys, higher coherency results in better-supported struts in architected polygrain-like meta-crystals. In particular, a higher number of total struts connecting to boundaries leads to higher strength. In addition to the influential role of boundary coherency, meta-grain boundaries are effective in shortening and deflecting the propagation of shear bands thanks to the lattice misorientation across meta-grain boundaries. It is worth noting that the yield strength was measured at a strain offset of 0.2% at which the first shear band already happens and experiences the stopping effect of meta-grain boundaries on the shear-band propagation, explaining the increase in macroscopically measured yield strength with decreasing the meta-grain size. An architected tilt type of boundaries induced higher hardening than an architected twist one, highlighting that different coherency of meta-grain boundaries results in different degrees of hardening. This implies that high strength architected materials can be designed without reducing the size of meta-grains. This finding is important because it is not always possible to keep reducing the size of meta-grains due to physical constraints. The engineering of boundary coherency similar to the grain engineering approach in metallurgy can enable the achievement of high strength and damage-tolerant architected materials. In addition, as this study only utilised the twin FCC45 boundary, the use of different misorientations and boundary characteristics with good connectivity to the boundary will enable additional freedom in tailoring the properties of architected materials.51. In metallurgy, the isotropic behaviour can be achieved by an aggregate of randomly oriented grains53. Most of the lattices are inherently anisotropic due to the directionality in bondings. The anisotropy is exaggerated for metallic structures fabricated by additive manufacturing due largely to columnar grain growth55. Considering the high anisotropy of lattice materials, increasing the number of domains of different crystallographic orientations, i.e. the polygrain-like meta-crystals, not only can increase the strength and energy absorption but also minimise the anisotropy of architected lattice materials. Such minimised anisotropy can improve the material performance in multiaxial and complex loading paths.The introduction of crystalline-inspired features to break the long-range periodicity in a singly orientated lattice into meta-grains of different orientations inhibits the fast propagation of the shear band and prevents catastrophic failure. It is worth noting that the improvement in the yield strength and post-yield behaviour induced by the polygrain-inspired lattice materials is in relation to single crystal-like ones with the former outperforms the latter one of the same lattice type, such as FCC in this study. Tremendous potential opportunities to strengthen and tailor the properties of meta-crystals remain to be explored when combining the reduction in meta-grain size, meta-grain morphology and distribution and the boundary type (e.g. low versus high-angle boundaries) with different types of lattice (e.g. Kagome and honeycomb instead of FCC). As the origin of the hardening effect results from the stopping and shortening of shear bands induced by the meta-grain boundaries, the boundary hardening approach should be easily translated to other unit-cell typesZ axis being parallel to the build direction of fabrication and also the loading direction of the compression test. To study the orientation dependence, three architected materials containing singly orientated lattices of the FCC unit cell  were designed. The first single meta-crystal was generated in two rotation steps as follows: the three X-, Y- and Z axes. The unit cell was then rotated 45° clockwise about the global X axis and followed by 30° clockwise about the global Y axis. The first single meta-crystal was then constructed by infilling a global cube with a side length of Y axis. In total, three singly orientated meta-crystals with their Y axis.A cubic unit cell with dimension of 29. The global lattice structure with dimensions of The same design approach as presented in our previous study was used to create polygrain-like architected materials called meta-crystalsins Fig. , the unimentclass2pt{minimins Fig.  while thted Fig. , dividinZ axis shown in Fig. The designed meta-crystal models were fabricated using fused deposition modelling (FDM) 3D printer (Ultimaker 2) with polylactic acid (PLA) filament obtained from RS components. Some main printing parameters were given as follows: layer thickness of Z axis shown in Fig. Z direction by the initial length X–Z plane shown in Fig. Compression tests were carried out by a The mechanical properties of the base material were obtained from the uniaxial tensile tests on specimens designed according to ISO 527 standard. Specimens were fabricated by FDM using the same printing parameters as the meta-crystals. Average elastic modulus Z axis which is subsequently resolved into two orthogonal forces: Z axis). Since the buckling is the main failure mode of slender struts, critical buckling force 56, where Z axis was Z axis and the direction of Z direction and the To understand the deformation behaviours of lattices under loading, we performed force analyses on the struts of FCC unit cells orientated to different directions with respect to the loading direction. The directions of individual struts were defined with respect to the local coordinate system of the unit cell ,,\documenFinite element analysis (FEA) was carried out to study the spatial deformation behaviour of internal architected struts in single meta-crystals using Abaqus/Explicit to assist the DIC analyses. Two FEA lattice models for FCC45 and FCC60 were designed with the dimensions for the unit cell and 2D frame remained the same , but a reduced global dimensions of Supplementary InformationPeer Review FileDescription of Additional Supplementary FilesSupplementary Movie 1Supplementary Movie 2


Label-free optical coherence tomography angiography (OCTA) has become a premium imaging tool in clinics to obtain structural and functional information of microvasculatures. One primary technical drawback for OCTA, however, is its imaging speed. The current protocols require high sampling density and multiple acquisitions of cross-sectional B-scans to form one image frame, resulting in low acquisition speed. Recently, deep learning (DL)-based methods have gained attention in accelerating the OCTA acquisition process. They achieve faster acquisition using two independent reconstructing approaches: high-quality angiograms from a few repeated B-scans and high-resolution angiograms from undersampled data. While these approaches have shown promising results, they provide limited solutions that only partially account for the OCTA scanning mechanism. Herein, we propose an integrated DL method to simultaneously tackle both factors and further enhance the reconstruction performance in speed and quality. We designed an end-to-end deep neural network (DNN) framework with a two-staged adversarial training scheme to reconstruct fully-sampled, high-quality (8 repeated B-scans) angiograms from their corresponding undersampled, low-quality (2 repeated B-scans) counterparts by successively enhancing the pixel resolution and the image quality. Using an in-vivo mouse brain vasculature dataset, we evaluate our proposed framework through quantitative and qualitative assessments and demonstrate that our method can achieve superior reconstruction performance compared to the conventional means. Our DL-based framework can accelerate the OCTA imaging speed from 16 to 256 The ability to resolve axial structure superiorly has led to various clinical applications, such as neurology, ophthalmology, dermatology, gastroenterology, and cardiology5. Furthermore, OCT has also been extended to functional angiography, namely, optical coherence tomography angiography (OCTA), to study vascular diseases. OCTA extracts the variations in OCT signals caused by the red blood cell (RBC) motion to contrast blood flow within vessels against the static tissue. This functional angiography is accomplished using a sequence of repeated B-scans taken from a single cross-sectional location6. The agent-free OCTA modality, owing to its micro-level spatial resolution and non-invasive nature, has enabled anatomical and functional imaging in various clinical applications10. However, one limitation of OCTA is acquisition speed, bound by the scanning principle employed, i.e., point-by-point raster scanning and the requirement of sequential B-scans. Since high-speed imaging is very desirable for functional imaging, a branch of study has emerged focusing on developing OCT systems with ultrahigh imaging speed, e.g., high-speed optical sources13 and detection devices16. However, enahncing imaging speed using hardware adjustments is cost-prohibitive and may pose limitations for practical clinical system implementations.Optical coherence tomography (OCT) is an essential biomedical imaging technique based on low-coherence interferometry to image biological tissues. It enables non-invasive, depth-resolved imaging by demodulating backscattered light interference17 adopted convolutional neural network (CNN) architectures to accelerate the B-scan repetition procedure by reconstructing high-quality cross-sectional angiograms (acquired from 48 consecutive B-scans) from a limited number of B-scans  and peak signal-to-noise ratio (PSNR) values of 0.63 and 20.82, respectively. Another work in Ref.18 conducted pixel-wise super-resolution in the enface plane using deep learning to reduce the sampling density while preserving the pixel resolution. Results have outperformed baseline filter-based methods and showed improved signal-to-noise ratio (SNR) and vascular connectivity.Recently, several studies have been conducted to enhance the OCTA imaging speed via advances of emerging deep learning (DL)-based methods. Such studies aim to provide software-only solutions that require no modifications to the hardware settings. A study in18 addressed only one of the mentioned factors. Secondly, a DL-framework specific to the application should be designed using state-of-the-art DL principles to enhance the desired performance. Previous works, while showed feasibility, mostly utilized already-existing networks designed for other applications . Hence, elaborate image-to-image translation techniques  should be considered to establish a framework suited for the current application.Although DL-based methods have demonstrated the effectiveness in accelerating the OCTA procedure, the following aspects require further investigation, which has motivated the current study. First, all factors related to the imaging speed  should be considered simultaneously in a single framework. The previous worksHere we present DL-based principles to accelerate the OCTA acquisition speed while addressing the above limitations. As mentioned before, our work differs from the previous studies in that we aim to tackle all factors related to the OCTA imaging speed , and thus enhance the pixel resolution and the image quality in a single integrated framework. By considering different model architectures, loss functions, and training strategies, we establish our networks using a two-staged adversarial training scheme to enhance the pixel resolution and the image quality successively. We evaluate our networks on an in-vivo mouse brain vasculature dataset and conduct a comparative study to select the best-performing model. Finally, we compare quantitative and qualitative results with baseline methods and examine our proposed framework’s reconstruction capability compared to the existing means.In this section, the OCTA dataset acquisition method is briefly described, followed by overall explanations of DL-based methodologies and the proposed approach’s workflow.7), the samples were sealed with glass coverslips and imaged using a custom-designed spectral-domain OCT system from the University of Washington Seattle8. The system utilizes a broadband superluminescent diode as the light source with a central wavelength of 1340 nm and a 3 dB spectral bandwidth of 110 nm. A biaxial galvanometer is utilized to raster scan the probing light using a scanning step size of 10 7 was used to register eight sequential B-scans at each slow-axis location to generate a high-quality cross-sectional angiogram. We chose to utilize eight sequential B-scans since it is known that the angiogram quality  saturates near eight repeated B-scans19. We downsampled the structural volumes in the enface plane, and two sequential downsampled B-scans were randomly selected to create a corresponding cross-sectional angiogram with low resolution and quality. The downsampling procedure was conducted to mimic the OCTA systems’ rasterized scanning principle. For example, if the artificial scanning step size is 2enface plane by a ratio of two. The fully-sampled angiograms’ enface pixel resolution was 400 enface representations. The enface angiograms from two sequential downsampled B-scans are low in resolution and quality. They are denoted as  for the remainder of the paper and used as the input data to our DNN framework. Similarly, the high-resolution, high-quality enface angiograms from eight sequential fully-sampled B-scans are denoted  and used as the target for training and ground truth for evaluation.  and  angiograms were paired according to the downsampling ratio.Our dataset comprises in-vivo brain microvasculature images of 8-week-old male mice (C57BL/6J). After removing the scalp and skull  to establish our DNNs. To address the limited data issue, we used 2000 depth-wise sectioned enface angiograms from seven volumetric pairs as our training and validation data which were randomly split at a ratio of 9:1. As illustrated in Fig. enface angiograms differ from MIP enface angiograms in that void spaces are common in the sectioned enface angiograms. Thus, we manually selected images with sufficient vessel profiles to use as our training and validation data. The training dataset was used for optimizing the stochastic gradient descent algorithm, and the validation dataset was used during training to monitor the loss value and finetune the hyper-parameter settings. Random rotation and flipping in the x and y axes were applied to augment the training dataset. As for the test data, 200 sectioned enface angiograms were manually selected from an independent volumetric pair to conduct a comparative study and select the best-performing method. Furthermore, 21 MIP enface angiograms from the remaining volumetric pairs were utilized to evaluate the selected methods. A summary description of the dataset is provided in Table The MIP MIP data 9 total tenface angiograms from their  counterparts. As shown in Fig. We have developed an end-to-end CNN framework to reconstruct  Deep layer. The Deep layer consists of five convolution blocks with  to their fully-sampled, low-quality counterparts . Note that  and  angiograms are constructed from two repeated B-scans and, therefore, of poor quality. Thus, the task of mapping  to  angiograms is conducting pixel-wise super-resolution to fill in the missing pixels from undersampling. The module first extracts low-level features from the input angiograms using . The module specifically compensates for the B-scan repetition number (from two repeated B-scans to eight) and thus enhances the angiograms quality. It is worth mentioning that the feature map size is preserved throughout the module as the input and the ground truth  angiograms are spatially aligned. For the most part, the input and the ground truth angiograms share similar low-frequency information but differ in the high-frequency details. Thus, we designed our module with (9 Deep layer: the DenseReconstNet (dense connection) and the ResReconstNet .As mentioned before, the modules are concatenated in series to form a single reconstruction network. We establish two comparative network models based on the inter-block connection type adopted in the L, we diverge from the de facto mean squared error (MSE) loss used in the previous studies18 and instead propose a perceptually improved loss function by combining different loss terms. While the MSE loss achieves high PSNR, minimizing the MSE loss encourages reconstructing pixel-wise averages of plausible outcomes, typically over-smooth and thus failing to capture the high-frequency details of small vessel profiles25. We utilize the mean absolute error (MAE) loss as the primary loss function since it is known to have better local convexity26:Several training strategies were employed to train our networks and optimize their trainable parameters. For the loss function N denotes the number of pixels in each angiogram, Y and 27) loss, with the 26. The second term is the Fourier MAE loss (FMAE), which calculates the MAE from the magnitude of the angiograms’ 2D Fourier transforms. We choose FMAE to provide information on the vessel orientations and perceive the downsampling remnants as frequency corruptions23. The general strategy in incorporating different loss terms is that the primary loss function reconstructs the overall structure and the angiograms’ low-frequency components. In contrast, the two sub-loss terms reconstruct the high-frequency details and remove the angiograms’ frequency-space artifacts. In total, our loss function L is designed by linearly combining counterparts and is defined as follows:The first term is the multiscale structural similarity  angiogram taken as the input to our reconstruction model. The idea is that we train our reconstruction model to fool the discriminator that distinguishes the reconstructed angiograms from their  counterparts. The proposed training strategy allows our reconstruction model to create perceptually superior solutions residing in the manifold of the  angiograms. The adversarial loss function for the reconstruction models is defined as follows30:25 and finetuned by heuristic searching and was found to be sufficient for all of the tested models. The established generative adversarial networks (GANs) are denoted as DenseReconstGAN and ResRecontGAN. Details on the discriminator network’s structure can be found in Supplementary Table 31 and their details can be found in Supplementary Table The weight value for the adversarial loss term was referenced from previous literature32, MS-SSIM27, and the PSNR to assess the reconstruction performance. In addition, we assess the angiograms’ contrast by calculating the root-mean-square (RMS) contrast33 and the vessel connectivity by calculating the ratio of connected flow pixels to the total number of skeletonized pixels19.As for the evaluation metric, we consider the perceptual indices of SSIM18. Implementation details for HARNet were carefully examined from the previous literature18, and its hyper-parameters were optimized using the random search strategy31  and the B-scan repetition number . However, no previous work has attempted to address both factors in a single integrated framework. Therefore, we compare our method with existing super-resolution methods, including interpolation-based image upsampling methods  and a previously reported DL-based super-resolution network for retinal OCT angiograms called the high-resolution angiogram reconstruction network (HARNet)enface angiograms do not contain full vascular profiles , we conduct a full ablation study in addition to the comparative study with the baseline methods, and quantitative results are summarized in Table see Fig. , and app35. It is worth mentioning that the previously reported HARNet shows poor reconstruction performance even compared to our single-stage models trained using the MAE loss. One possible reason could be that while it is known that pre-upsampling the input image using filter-based methods deteriorates the results25, our networks learn the upsampling filters directly in contrast to HARNet. DenseReconstGAN consistently shows the best performance amongst all comparative methods for each of the downsampling ratios. HARNet shows the best performance amongst the baseline methods, and the bicubic interpolation method outperforms all other interpolation-based methods. Therefore, we focus our analysis on comparing the three methods for assessing the MIP test dataset, and the results are presented in the following section.The models trained with our proposed training strategy (ResReconstGAN and DenseReconstGAN) outperform all baseline methods, including the interpolation-based methods and the previously reported HARNet. When solely using the MAE loss, the networks trained with the two-staged training scheme show improved performance over those trained in a single-stage manner that directly maps  angiograms to their  counterparts, including HARNet. The two-staged training strategy enhances the reconstruction performance since it provides additional intermediate supervision during training to perform super-resolution to  angiograms. Furthermore, Table enface images taken from various biosamples. The quantitative results of the three comparison methods are presented in Table enface images, can generalize to full vascular profiles in MIP enface images. However, we notice that all evaluation metrics using the MIP enface dataset are lower than the sectioned enface dataset , it can be seen that HARNet suffers from contrast loss and oversmooths the vascular details, resulting in dilated vessel structures. In contrast, our DenseReconstGAN demonstrates improved reconstruction precision with obvious advantages in recovering the high-frequency microvascular details owing to our improved training strategy. Since capillary morphology provides critical information about vascular pathology , we specifically compare the reconstruction performance of very small vessels , while our DenseReconstGAN reconstructs smoother and rounder vessel profiles. However, at such a high downsampling ratio, the performance of our DL model deteriorates severely. Since the downsampling ratio of eight is equivalent to a scanning step size of 80 38), the degraded results are expected. Thus, the desired application context should always be considered when selecting the appropriate scanning step size to meet the desired performance. For example, a large scanning step size  and using two sequential B-scans would result in a 16We also compare the computation time and the mean frames per second (FPS) rate is provided in Table This study proposed a deep learning-based software-only solution to accelerate the OCTA systems’ imaging speed while preserving the image quality. The main contributions of the work are summarized as follows. First, we tackled all existing inherent factors related to the slow OCTA imaging speed, including the B-scan repetition number and the sampling density. While previous works only partially addressed the limiting factors, our work considered all aspects in a single, end-to-end framework. As a result, our approach achieved an acceleration of OCTA imaging by factors of up to 16–256enface images from various in-vivo samples. We also aim to extend our framework to other pathological applications . By combining our established framework with transfer learning techniques39, we can avoid acquiring a large amount of data that is required for re-training. We hope to utilize our integrated framework to broaden OCTA systems’ clinical applicability to rapidly and accurately delineate pathological biomarkers and achieve potential near real-time functional imaging capability.In the future, we aim to continually refine our DL framework’s generalizability by training with more MIP Supplementary Tables.


Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has circulated throughout the world causing the worst pandemic since 1918. All efforts have been marshalled towards testing different treatment approaches, obtaining clinical and epidemiological information, developing suitable diagnostic tests, and developing new vaccines. New ribonucleic acid (RNA)-based and viral vector-based vaccines have been developed and licensed under emergency use in many countries; however, there is a huge demand for vaccines, and it will take some time before a sufficient number of people are vaccinated to stop the circulation of the virus. Therefore, the proper diagnosis and identification of infected individuals are crucial for the isolation and treatment of these patients and tracing of their contacts. Many diagnostic tests and diag-nostic kits have been developed in a relatively short time. This review summarizes the principles of the available laboratory assays that are in use for the detection of SARS-CoV-2 RNA, antigens, or antibodies. The pandemic of coronavirus disease 2019 (COVID-19) continues to evolve during 2020 affecting life, health, and economy worldwide, making it the worst pandemic since the influenza pandemic in 1918. In addition to millions of infections and millions of deaths, many millions of people have lost their jobs . SARS-CoThe early diagnosis of infected individuals and tracing of their contacts, to isolate them and protect susceptible individuals, are integral factors to controlling infection and containing the pandemic. COVID-19 signs and symptoms such as fever, dry cough, fatigue, and shortness of breath are common among other respiratory tract infections such as SARS, MERS, and influenza . MoreoveLaboratory diagnostic tests can identify apparent and asymptomatic infections and facilitate surveillance programs by detecting the viral nucleic acid, the viral antigens, or the antibodies against the viral antigens. The laboratory diagnosis of SARS-CoV-2 infection can not only play a role in identifying new infections but also in understanding the spread of the disease, the level of its containment in a community, and the level of immunity in a population.Since the emergence of SARS-CoV-2, unprecedented efforts have been made to develop different types of laboratory assays at exceptional speeds. A laboratory developed test (LDT) for the detection of the viral RNA was available very soon after the announcement of the viral genome sequence . Later, Several blood parameters have been used to reflect the association with SARS-CoV-2 infection. In general, patients infected with SARS-CoV-2 have normal or more commonly decreased leukocyte counts with lymphocytopenia , 9], , , 11], , . However, , 11], C-reactive protein (CRP) is an acute-phase protein expressed at high concentrations during acute inflammatory infection . CRP hasD-dimer is a product of fibrin breakdown during the degradation of vascular thrombi and is therefore a good indicator of hemostatic abnormalities and intravascular thrombosis . Normal Other blood biomarkers have also been found to be elevated in COVID-19 patients; however, these other blood biomarkers are not specific indicators of infection but are more useful in the prognosis of disease progression.Suitable samples and proper sampling are crucial for the molecular detection of respiratory viruses. Different types of respiratory tract samples are used to diagnose SARS-CoV-2 infection. A higher rate of positive results is obtained from lower respiratory tract samples; therefore, sputum samples are the better samples when available . If a spBetacoronavirus genus and sequencing of another genomic fragment for SARS-CoV-2 . A. A47]. ASeveral studies have compared commercially available assays. A comparison between multiple commercially available kits  and the protocol designed by the Centers for Disease Control and Prevention (CDC) for SARS-CoV-2 detection based on the N1 and N2 regions showed that all of the assays were 100% specific, and the Cepheid kit had 100% agreement with the CDC protocol . The AbbIt is worth noting, however, that a positive PCR result does not necessarily indicate the presence of infectious virus particles. A study suggested that a cycle threshold (Ct) equal to or more than 24 after 8 days from symptoms onset might predict the presence of noninfectious virus particles . AnotherDroplet digital PCR (ddPCR) is quantitative PCR utilizing standard PCR components with two main differences: first, the PCR reaction is performed in thousands of individually fractionated reaction droplets and second, the reaction results is read at an endpoint, unlike the real-time PCR . These tIn a comparison between real-time PCR and ddPCR for the detection of SARS-CoV-2, the ddPCR detected SARS-CoV-2 in 26 samples that were negative by real-time PCR and had improved sensitivity, negative predictive value, negative likelihood ratio, and accuracy . MoreoveThe loop-mediated isothermal amplification (LAMP) assay was developed in 2000 to overcome the shortcomings of other nucleic acid amplification methods such as the need for complicated instruments for target amplification and detection of the product, as for PCR; the use of expensive modified nucleotides, as for strand displacement amplification; or compromised specificity due to low amplification temperature, as in self-sustained sequence replication and sequence-based amplification . The LAMIn combination with reverse transcription, the LAMP assay can also be used to detect RNA targets . NumerouThe ID NOW COVID-19 assay from Abbott is another FDA-approved test based on LAMP technology (see Rapid Point-of-Care Assays below).The need for prompt identification of SARS-CoV-2-infected individuals is crucial to put patients on appropriate management regimens and to reduce the transmission risk. Two rapid diagnostic assays were approved by the FDA for diagnostic use under emergency authorization Table 1 . The ID The Accula SARS-CoV-2 Test is another FDA-approved rapid test that can yield results within 30 minutes . The assAs mentioned, the false-negative RT-PCR results for SARS-CoV-2, despite the presence of suggestive clinical disease manifestations, constitute a major challenge for the medical staff and increase the load on infection control officers and isolation rooms in hospitals. The humoral immune response against SARS-CoV-2 may help in confirming the diagnosis. Immunoglobulin M (IgM) and IgA are reported to appear within 5 days from symptom onset in 85.4% and 92.7% of patients, respectively , 51]. I. I51]. IDetection of SARS-CoV-2 antibodies has a number of advantages in addition to improving the total detection rate in COVID-19 patients. Antibody detection can help in estimating the actual disease prevalence in the population. Immunological assays detecting antibodies can be performed on a large scale, allowing for mass testing of the population. Furthermore, the detection of antibodies in COVID-19 patients may help in predicting disease prognosis. Patients with severe symptoms have been reported to have higher IgM antibody levels for a longer period of time , 77]..77].Immunological tests can detect either virus antigens or the antibodies against these antigens produced as an immune response to infection.This type of assay looks for virus proteins in respiratory samples from patients  using an antibody specific to a certain target and different methods to detect the antigen-antibody (Ag-Ab) complexes, such as the immunofluorescence or lateral flow assays.Multiple Ag-based immunoassays received the FDA approval under the emergency authorization such as the Sofia 2 SARS Antigen FIA, BinaxNOW COVID-19 Ag Card, and BD Veritor System for Rapid Detection of SARS-CoV-2 . Numerous forms of EIA are available on the market. The FDA approved three types of such assays for testing of patients with COVID-19: the rapid immunoassays, Chemiluminescence immunoassays (CLIA), and the enzyme-linked immunosorbent assays (ELISA).Some rapid immunoassays employ lateral flow technology to detect the presence of IgM, IgG, or most commonly IgG and IgM antibodies simultaneously, against SARS-CoV-2. These assays are based on the liquid chromatography principle whereby the antibodies in patient samples migrate in flow with the buffering reagents on a nitrocellulose membrane. Detection components are immobilized on certain pre-marked locations on the nitrocellulose membrane. The rapid immunoassays can deliver results within 15 minutes and are simple, can be performed without the need for expensive equipment, and show good performance in comparison to conventional immunoassays . The mulThe most commonly used EIAs in clinical laboratories are the enzyme-linked immunosorbent assay (ELISA) and the Chemiluminescence immunoassay (CLIA). ELISA and CLIA are sensitive assays and can be used to test a large number of samples in one run, which makes them suitable for laboratories with high throughput requirements. They are also available in fully automated system formats and in manual formats. Some CLIAs have an advantage over ELISA in terms of turnaround time and analytical sensitivity. These assays can be designed to detect total SARS-CoV-2 antibodies or antibodies against one of its protein components such as the S protein or the nucleocapsid protein. It has been reported that the EIAs that detect anti-nucleocapsid protein antibodies provide better sensitivity than those that detect anti-spike protein antibodies . HoweverThe enzyme immunoassays, in general, have shown very good performance in the detection of antibodies after day 10 of infection, with compromised sensitivity during days 5–9 of the infection , 90]. T. T90]. TWhile other immunoassays detect the total antibody spectrum against a virus or certain of its components, the virus neutralization assay (VNA) distinguishes the fraction of antibodies that interfere with the binding of the virus to its cellular receptor, inhibits cell entry, and neutralizes its infectivity; hence called neutralizing antibodies. In case of SARS-CoV-2, the neutralizing antibodies are directed against the receptor-binding domain (RBD) in the spike protein of the virus. The VNA is very helpful during the emergence of a new virus when no commercial assays are available to detect the presence of virus-specific antibodies. It is also helpful in determining whether a convalescent plasma contains protective neutralizing antibodies against the virus .Conventional VNA is performed by mixing the whole virus in cell culture supernatant with the serum of a patient and measuring the virus infectivity to target cells compared to a control without patient serum. The test results are then measured by the level of damage the virus causes to the cell (cytopathic effect) . HoweverDespite the diagnostic and research utility of the VNA, it requires cell culture facility and specially trained personnel and therefore would not be convenient for commercial production.Prompt diagnosis of COVID-19 cases is crucial for isolation, treatment, and contact tracing. Molecular diagnostic assays constitute by far the first line of defense for the identification of infected individuals. Immunological assays are mostly negative at the beginning of symptoms and cannot therefore play a significant role in the diagnosis of infected patients, but rather in defining the immunological status of a population at risk, such as health care workers, or the general population, particularly with the reports of frequent asymptomatic infection.We additionally urgently need to produce more sensitive assays to reduce false-negative results and their deleterious effects on the spread of the virus in the human population.The author declares that he has no competing interests.


Cu-bearing age-hardening steel has significant potential in shipbuilding applications due to its excellent weldability as compared to conventional NiCrMoV steel. Not much research has been carried out to analyze the differences in the mechanisms of strength and toughness between Cu-bearing age-hardening and NiCrMoV steel. Both steels were heat treated under the same conditions: they were austenized at 900 °C and then quenched to room temperature, followed by tempering at 630 °C for 2 h. The uniaxial tensile test reveals that the Cu-bearing age-hardening steel exhibits relatively lower strength but larger plasticity than NiCrMoV steel. The lower contents of Carbon and other alloying elements is one of possible reasons for these differences in mechanical properties. Transmission Electron Microscope observations show that two types of precipitates, Cr carbides and Cu-rich particles, exist in tempered Cu-bearing age-hardening steel. Cu-rich particles with sizes of 20–40 nm can inhibit the dislocation motion during deformation, which then results in dislocation pile ups and multiplication; this makes up the strength loss of Cu-bearing age-hardening steel and simultaneously improves its plasticity. The development of shipbuilding steels has gone through a process from carbon steel to low-carbon low-alloy steel, and further to ultra-low-carbon low-alloy steel ,2. The pSince the 1950s, NiCrMoV serial shipbuilding steels such as HY80 and HY100 have been successively developed, in which the carbon content was generally controlled to a level lower than 0.18%, and the addition of Ni, Cr and Mo elements contributed to the improvement of hardenability . HoweverIt has been reported that when the addition of the Cu element was higher than 0.6%, the supersaturated Cu in steel would result in the precipitation of ε-Cu particles during the aging treatment, which could then significantly improve the strength of steel . For exaAlthough some research studies on Cu-bearing age-hardening steel have been reported, its strengthening and toughening mechanism remains unclear due to the absence of comparative studies with traditional NiCrMoV shipbuilding steels. In this paper, an ultra-low-carbon Cu-bearing age-hardening steel developed in our previous studies was subjected to the same heat treatment with NiCrMoV steel, and then the microstructural difference between them was analyzed in detail, with the aim of exploring the effect of microstructural difference on the strength and toughness mechanism.The hot-rolled sheets of Cu-bearing age-hardening steel and NiCrMoV steel were used in this study. The chemical compositions of the studied steels are listed in The studied steels were solution-treated for 1 h at 900 °C, followed by water quenching. The quenched slabs were tempered at 630 °C for 2 h and then air-cooled to room temperature. In order to facilitate the comparison of the experimental results, the heat treatment of both Cu-bearing age-hardening steel and NiCrMoV steel were carried out under the same conditions. The samples used for microstructure observations and tensile tests were then machined from the heat-treated slabs by wire-electrode cutting. The thickness of the tensile sample was 2 mm. Detailed size and the schematic diagrams for microstructure observations can be found in The tensile tests were performed at room temperature on a Zwick/roell Z100 universal testing machine . The extensometer was used to track the strain to failure. The loading force, displacement and other parameters were recorded by the software during the testing process. The initial rate for the tests was set as 2 mm/min. Three samples for each steel were tested in order to achieve more accurate results.Standard metallographic procedure was applied prior to the microstructure observation. The surface of all specimens was polished down to 1µm with water-based diamond suspensions and, at the end of the process, cleaning with acetone and drying in air were undertaken. Nital etchant containing 5% nitric acid was used for etching. The EBSD (Electron Back-Scattered Diffraction) samples were prepared by electropolishing with 5% perchloric acid alcohol solution. The voltage was set to 30 V and the polishing time was about 12 s. Ion etching was employed for the preparation of the TEM  samples with 8% perchloric acid alcohol solution. The microstructure observations via SEM (scanning electron microscope) were carried out on a HITACHI SU5000  with an OXFORD HKL  detector for the EBSD test and EDS (Energy Dispersive Spectroscopy) analysis. As well as an optical microscope  equipped with a live camera, the CHANNEL 5 software  was employed for the EBSD data analysis. The microstructure, tensile fracture and precipitates were further observed using a transmission electron microscope .The engineering stress–strain curves obtained from tensile tests of tempered samples are shown in During tempering, the decomposition of bainite or martensite occurred in both studied steels, which resulted in the achievement of granular bainite in Cu-bearing steel but tempered martensite in NiCrMoV steel c,d. GraiAs mentioned above, the addition of Cu, and subsequently ε-Cu, particles precipitated during such heat treatment processes as tempering or aging will significantly improve the strength of Cu-bearing steels ,12,13. I23C6 by measuring and calculating the interplanar spacing of diffraction spots in combination with EDS analysis.In addition, the lath boundaries can also serve as nucleation sites for the precipitates during heat treatment. As a result, some fine precipitates (about 150 nm in size) distributed along the bainite ferrite laths can be found in It has been reported that ε-Cu or Cu-rich particles will precipitate from the supersaturated matrix when the Cu-bearing steel has been aged at a temperature higher than a certain value ,17. As aAs shown in Different from those Cr carbides precipitated along the bainite lath boundaries in In addition, due to the addition of Nb or other alloying elements in the Cu-bearing age-hardening steel, Nb particles may also precipitate during the aging of the steel, except during Cu precipitation. It has been reported that the Nb-carbonitride precipitation may also promote the precipitation of Cu particles during aging treatment . NbC pre23C6 particles.Note that the carbon content of Cu-bearing age-hardening steel and NiCrMoV steel in our present work is 0.037 and 0.077%, respectively. The solid solution strengthening effect resulting from carbon may be negligible. Furthermore, there are only trace additions of Ti or Nb in the studied steels; the precipitation hardening caused by possible precipitates such as Ti or Nb carbonitrides is also not significant. Therefore, the additions of the Ni, Cr and Mo elements play an essential role in the enhancement of the strength of both steels due to their effective improvement of hardenability. This is the primary reason that some martensite or bainite laths can still be found in both steels even after tempering. Nevertheless, the carbon and other alloying elements in Cu-bearing steel are relatively lower than those in NiCrMoV steel, resulting in small amounts of granular bainite appearing in Cu-bearing steel due to the insufficient hardenability.The TEM analysis in Although Cr carbides have been found in both the tested steels, with particle sizes of about 120–150 nm, finer Cu-rich phase particles of about 20–40 nm in size are only present in the Cu-bearing age-hardening steel. Furthermore, Cu-rich phase particles are relatively small in size and mainly distributed in the ferrite matrix. During plastic deformation, these particles play the role of pinning the dislocations and other defects, which is verified by the TEM observations in The presence of second-phase particles exerts a certain influence on the strength and plasticity of material. It is generally believed that the strength of a material can be effectively improved when fine second-phase particles are dispersed in the matrix. Moreover, the research results for Nano steel show that a reasonable size and distribution of second-phase particles can also enhance the plasticity and toughness of steels at the same time ,22,23. TAs compared to conventional NiCrMoV steel, the Cu-bearing age-hardening steel exhibits a relatively poor hardenability. After quenching and subsequent tempering, the microstructure of Cu-bearing age-hardening steel consists largely of bainite.Cr carbides have been found in both tested steels, with particle sizes of about 120–150 nm. However, some finer Cu-rich phase particles of about 20–40 nm in size can also been found in the Cu-bearing age-hardening steel.Cu plays a leading role in precipitation hardening, making up for the strength loss in Cu-bearing age-hardening steel due to the content reduction of carbon and other alloying elements. The interactions between Cu particles and dislocations result in dislocation multiplication, which contributes to the better plasticity of Cu-bearing age-hardening steel.The study was focused on the differences between Cu-bearing age-hardening steel and NiCrMoV steel in terms of their strengthening and toughening mechanisms. It can be concluded from the experimental study and analysis that: