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At least 145 records · Page 8

Contributions of vegetation heterogeneity within tower footprint to CO 2 flux estimations through graph neural network modeling

Net ecosystem exchange of CO 2 (Fc) measured directly by eddy covariance towers is based on various assumptions, including large, flat and homogenous land cover type. In reality, often a tower site is not large enough for flux measurements, and landscapes consist of patches of different land cover types within the flux footprint. In addition, some portions of fluxes are contributed by different cover types when a footprint exceeds the size of the target ecosystem. The contributions of non-dominant patches to Fc are often ignored. Here, in this study, we propose a novel integrated modeling framework that combines random forest (RF) and XGBoost with a residual correction module based on a deep graph convolutional network (DeeperGCN) to simulate Fc for seven flux measurement sites in southwest Michigan. High-resolution remote sensing vegetation indices, soil properties, meteorological variables, and footprint-weighted spatial features were used as model inputs at three spatial resolutions (10 m, 20 m, 30 m), and their importance in predicting Fc with DeeperGCN was assessed. We found that residual correction using DeeperGCN significantly improved prediction accuracy, with the R 2 increasing from 0.9098 to 0.9479 for RF and from 0.9235 to 0.9433 for XGBoost. At site level, the maximum improvement in R 2 reached 0.1617. Paired t-tests confirmed that these improvements were statistically significant (p < 0.05). Among all predictors, leaf area index and incoming shortwave radiation emerged as the dominant drivers of spatial residual variation, followed by precipitation, relative humidity, and selected vegetation indices. The 20 m resolution yielded the best balance between model performance and computational efficiency. In conclusion, our modeling framework effectively captures both spatial heterogeneity and nonlinear interactions, offering a robust solution for spatially explicit flux modeling in structurally diverse ecosystems beyond the study sites.

footprint model↗

Infrared Optical Anisotropy in Quasi‐1D Hexagonal Chalcogenide BaTiSe 3

Polarimetric infrared (IR) detection bolsters IR thermography by leveraging the polarization of light. Optical anisotropy, i.e., birefringence and dichroism, can be leveraged to achieve polarimetric detection. Recently, giant optical anisotropy is discovered in quasi-1D narrow-bandgap hexagonal perovskite sulfides, A 1+x TiS 3 , specifically BaTiS 3 and Sr 9/8 TiS 3 . In these materials, the critical role of atomic-scale structure modulations in the unconventional electrical, optical, and thermal properties raises the broader question of the nature of other materials that belong to this family. To address this issue, for the first time, high-quality single crystals of a largely unexplored member of the A 1+x TiX 3 (X = S, Se) family, BaTiSe 3 are synthesized. Single-crystal X-ray diffraction determined the room-temperature structure with the P31c space group, which is a superstructure of the earlier reported P6 3 /mmc structure. The crystal structure of BaTiSe 3 features antiparallel c-axis displacements similar to but of lower symmetry than BaTiS 3 , verified by the polarization dependent Raman spectroscopy. Fourier transform infrared (FTIR) spectroscopy is used to characterize the optical anisotropy of BaTiSe 3 , whose refractive index along the ordinary (E ⊥ c) and extraordinary (E ‖ c) optical axes is quantitatively determined by combining ellipsometry studies with FTIR. With a giant birefringence Δn ∼ 0.9, BaTiSe 3 emerges as a new candidate for miniaturized birefringent optics for mid-wave infrared to long-wave infrared imaging.

optical anisotropy↗

Automated prediction of lattice parameters from X-ray powder diffraction patterns

A key step in the analysis of powder X-ray diffraction (PXRD) data is the accurate determination of unit-cell lattice parameters. This step often requires significant human intervention and is a bottleneck that hinders efforts towards automated analysis. This work develops a series of one-dimensional convolutional neural networks (1D-CNNs) trained to provide lattice parameter estimates for each crystal system. A mean absolute percentage error of approximately 10% is achieved for each crystal system, which corresponds to a 100- to 1000-fold reduction in lattice parameter search space volume. The models learn from nearly one million crystal structures contained within the Inorganic Crystal Structure Database and the Cambridge Structural Database and, due to the nature of these two complimentary databases, the models generalize well across chemistries. A key component of this work is a systematic analysis of the effect of different realistic experimental non-idealities on model performance. It is found that the addition of impurity phases, baseline noise and peak broadening present the greatest challenges to learning, while zero-offset error and random intensity modulations have little effect. However, appropriate data modification schemes can be used to bolster model performance and yield reasonable predictions, even for data which simulate realistic experimental non-idealities. In order to obtain accurate results, a new approach is introduced which uses the initial machine learning estimates with existing iterative whole-pattern refinement schemes to tackle automated unit-cell solution.

42 ENGINEERING↗

Lewis Acid Site Engineering in Chromite Spinels Orchestrated Surface Reconstruction and Surpasses RuO 2 in Oxygen Evolution

Atomic-scale engineering of chromite spinels featuring redox-active tetrahedral A-sites and strong Cr–O covalency offers a promising route to superior platinum-group-metal-free oxygen evolution reaction (OER) catalysts. However, comprehensive studies addressing how cation substitution influences surface chemistry and governs OER activity and durability in chromite spinels remain limited. Here, in this work, a systematic investigation of the multicationic chromite series Ni x Fe y Cr 3−x−y O 4 is presented, identifying composition-dependent Lewis acidity as a descriptor of superior OER performance. It is further demonstrated that tuning surface acidity directly controls dynamic reconstruction processes and lattice-oxygen participation during spinel-based electrocatalysis. Following activation, the optimized Ni 0.8 Fe 0.3 Cr 1.9 O 4 catalyst delivers a current density of 10 mA cm −2 at an overpotential of 235 mV, surpassing RuO 2 , with excellent long-term stability. Integrating microscopic and spectroscopic analysis with operando impedance spectroscopy, it shows that activation generates an oxyhydroxide overlayer and reveals a previously unrecognized link between surface Lewis acidity and the growth kinetics and activity of these shells. Density functional theory calculations indicate that Fe incorporation at octahedral sites raises the O 2p-band center and lowers oxygen-vacancy formation energy, promoting lattice-oxygen activation and triggering reconstruction, yielding enhanced OER. This work integrates cation-driven surface-acidity modulation, acidity-governed reconstruction, and OER activity enhancement into a unified predictive framework for designing earth-abundant spinel-based catalysts.

operando impedance spectroscopy↗

Development of a plant carbon–nitrogen interface coupling framework in a coupled biophysical-ecosystem–biogeochemical model (SSiB5/TRIFFID/DayCent-SOM v1.0)

Plant and microbial nitrogen (N) dynamics and N availability regulate the photosynthetic capacity and capture, allocation, and turnover of carbon (C) in terrestrial ecosystems. Studies have shown that a wide divergence in representations of N dynamics in land surface models leads to large uncertainties in the biogeochemical cycle of terrestrial ecosystems and then in climate simulations as well as the projections of future trajectories. In this study, a plant C–N interface coupling framework is developed and implemented in a coupled biophysical-ecosystem–biogeochemical model (SSiB5/TRIFFID/DayCent-SOM v1.0). The main concept and structure of this plant C–N framework and its coupling strategy are presented in this study. This framework takes more plant N-related processes into account. The dynamic ratio (CNR) for each plant functional type (PFT) is introduced to consider plant resistance and adaptation to N availability to better evaluate the plant response to N limitation. Furthermore, when available N is less than plant N demand, plant growth is restricted by a lower maximum carboxylation capacity of RuBisCO (V c,max ), reducing gross primary productivity (GPP). In addition, a module for plant respiration rates is introduced by adjusting the respiration with different rates for different plant components at the same N concentration. Since insufficient N can potentially give rise to lags in plant phenology, the phenological scheme is also adjusted in response to N availability. All these considerations ensure a more comprehensive incorporation of N regulations to plant growth and C cycling. This new approach has been tested systematically to assess the effects of this coupling framework and N limitation on the terrestrial carbon cycle. Long-term measurements from flux tower sites with different PFTs and global satellite-derived products are employed as references to assess these effects. The results show a general improvement with the new plant C–N coupling framework, with more consistent emergent properties, such as GPP and leaf area index (LAI), compared to the observations. The main improvements occur in tropical Africa and boreal regions, accompanied by a decrease in the bias in global GPP and LAI by 16.3 % and 27.1 %, respectively.

54 ENVIRONMENTAL SCIENCES↗

HarDWR - Harmonized Water Rights Records

For a detailed description of the database of which this record is only one part, please see the HarDWR meta-record. Here we present a new dataset of western U.S. water rights records. This dataset provides consistent unique identifiers for each spatial unit of water management across the domain, unique identifiers for each water right record, and a consistent categorization scheme that puts each water right record into one of 7 broad use categories. These data were instrumental in conducting a study of the multi-sector dynamics of intersectoral water allocation changes through water markets (Grogan et al., in review). Specifically, the data were formatted for use as input to a process-based hydrologic model, WBM, with a water rights module (Grogan et al., in review). While this specific study motivated the development of the database presented here, U.S. west water management is a rich area of study (e.g., Anderson and Woosly, 2005; Tidwell, 2014; Null and Prudencio, 2016; Carney et al, 2021) so releasing this database publicly with documentation and usage notes will enable other researchers to do further work on water management in the U.S. west. The raw downloaded data for each state is described in Lisk et al. (in review), as well as here. The dataset is a collection of objects stored within an RDate file, stateWaterRightsHarmonized.RData. Each object either describes how one should interact with the other objects, or contains the harmonized water rights data. Here is each object described in detail: states: A character vector containing the state names for those states in which data was collected for. More importantly, the index of the state name is also the index in which that state's data can be found in the various following list objects. For example, if California is the third index in this object, the data for California will also be in the third index for each accompanying list. rightsByState_ground: A list of data frames with the cleaned ground water rights collected from each state. The column headers for each data frame are: waterRightID - The unique identifying ID of the water right, the same identifier as its state uses. basinNum - The alpha-numeric identifier of the WMA the record belongs to. priorityDate - The priority date associated with the right. allocatedFlow - The maximum flow of the allocation in cubic feet per second (ft3s-1). source - Whether the right is for surface water or groundwater. origWaterUse - The original stated water use from the state. waterUse - The water use category under the unified use categories established here. rightsByState_surface: A list of the cleaned surface water rights collected from each state. See rightsByState_ground for more details on how the data is formatted. fullRightsRecs: A list of the combined groundwater and surface water records for each state. Essentially, these lists are the merging of rightsByState_ground and rightsByState_surface by state. See rightsByState_ground for more details on how the data is formatted. projProj: The spatial projection used for map creation in the beginning of the project. Specifically, the World Geodetic System (WGS84) as a coordinate reference system (CRS) string in PROJ.4 format. wmaStateLabel: The name and/or abbreviation for what each state legally calls their WMAs. h2oUseByState: A list of spatial polygon data frames which contain the area(s) in which each water right is claimed to be used. It should be noted that not all water right records have a listed area(s) of use in this object. Currently, only Idaho and Washington provided valid data to be included in this object. h2oDivByState: A list of spatial points data frames which identifies the location of the Point of Diversion for the state's water rights. It should be noted that not all water right records have a listed Point of Diversion in this object. spatialWMAByState: A list of spatial polygon data frames which contain the spatial WMA boundaries for each state. The only data contained within the table are identifiers for each polygon. It is worth reiterating that Arizona is the only state in which the surface and groundwater WMA boundaries are not the same. wmaIDByState: A list which contains the unique ID values of the WMAs for each state. plottingDim: A character vector used to inform mapping functions for internal map making. Each state is classified as either "tall" or "wide", to maximize space on a typical 8x11 page.

Economics↗

Vertically stacked photovoltaic and thermal solar cell

According to some embodiments, the present invention provides a novel photovoltaic solar cell system from photovoltaic modules that are vertically arrayed in a stack format using thin film semiconductors selected from among organic and inorganic thin film semiconductors. The stack cells may be cells that are produced in a planar manner, then vertically oriented in an angular form, also termed herein tilted, to maximize the light capturing aspects. The use of a stack configuration system as described herein allows for the use of a variety of electrode materials, such as transparent materials or semitransparent metals. Light concentration can be achieved by using fresnel lens, parabolic mirrors or derivatives of such structures. The light capturing can be controlled by being reflected back and forth in the photovoltaic system until significant quantities of the resonant light is absorbed. Light that passes to the very end and can be reflected back through the device by beveling or capping the end of the device with a different refractive index material, or alternatively using a reflective surface. The contacting between stacked cells can be done in series or parallel. According to some embodiments, the present invention uses a concentrator architecture where the light is channeled into the cells that contain thermal fluid channels (using a transparent fluid such as water) to absorb and hence reduce the thermal energy generation.

14 SOLAR ENERGY↗

Initial Steps Towards a Clinical FLASH Radiotherapy System: Pediatric Whole Brain Irradiation with 40 MeV Electrons at FLASH Dose Rates

In this work, we investigated the delivery of a clinically acceptable pediatric whole brain radiotherapy plan at FLASH dose rates using two lateral opposing 40-MeV electron beams produced by a practically realizable linear accelerator system. The EGSnrc Monte Carlo software modules, BEAMnrc and DOSXYZnrc, were used to generate whole brain radiotherapy plans for a pediatric patient using two lateral opposing 40-MeV electron beams. Electron beam phase space files were simulated using a model of a diverging beam with a diameter of 10 cm at 50 cm SAD (defined at brain midline). The electron beams were collimated using a 10-cm-thick block composed of 5 cm of aluminum oxide and 5 cm of tungsten. For comparison, a 6-MV photon plan was calculated with the Varian AAA algorithm. Electron beam parameters were based on a novel linear accelerator designed for the PHASER system and powered by a commercial 6-MW klystron. Calculations of the linear accelerator's performance indicated an average beam current of at least 6.25 µA, providing a dose rate of 115 Gy/s at isocenter, high enough for cognition-sparing FLASH effects. The electron plan was less homogenous with a homogeneity index of 0.133 compared to the photon plan's index of 0.087. Overall, the dosimetric characteristics of the 40-MeV electron plan were suitable for treatment. In conclusion, Monte Carlo simulations performed in this work indicate that two lateral opposing 40-MeV electron beams can be used for pediatric whole brain irradiation at FLASH dose rates of >115 Gy/s and serve as motivation for a practical clinical FLASH radiotherapy system, which can be implemented in the near future.

62 RADIOLOGY AND NUCLEAR MEDICINE↗

First demonstration of a TES based cryogenic Li 2 MoO 4 detector for neutrinoless double beta decay search

Cryogenic calorimetric experiments to search for neutrinoless double-beta decay (0vββ) are highly competitive, scalable and versatile in isotope. The largest planned detector array, CUPID, is comprised of about 1500 individual Li 2 100 MoO 4 detector modules with a further scale up envisioned for a follow up experiment (CUPID-1T). In this article, we present a novel detector concept targeting this second stage with a low impedance TES based readout for the Li 2 MoO 4 absorber that is easily mass-produced and lends itself to a multiplexed readout. We present the detector design and results from a first prototype detector operated at the NEXUS shallow underground facility at Fermilab. The detector is a 2-cm-side cube with 21 g mass that is strongly thermally coupled to its readout chip to allow rise-times of ~0.5 ms. This design is more than one order of magnitude faster than present NTD based detectors and is hence expected to effectively mitigate backgrounds generated through the pile-up of two independent two neutrino decay events coinciding close in time. Together with a baseline resolution of 1.95 keV (FWHM) these performance parameters extrapolate to a background index from pile-up as low as 5 • 10 –6 counts/keV/kg/yr in CUPID size crystals. The detector was calibrated up to the MeV region showing sufficient dynamic range for 0vββ searches. In combination with a SuperCDMS HVeV detector this setup also allowed us to perform a precision measurement of the scintillation time constants of Li 2 MoO 4 , which showed a primary component with a fast O(20 μs) time scale. Similar content being viewe

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

Spatiotemporal Modeling of Real World Backsheets Field Survey Data: Hierarchical (Multilevel) Generalized Additive Models

Assessing photovoltaic module backsheet durability is critical to increasing module lifetime. Laboratory-based accelerating testing has recently failed to predict large scale failures of widely adopted polymeric materials. Additionally, there is a growing concern on characterizing the non-uniformity of field exposure. Therefore, data from field surveys are critical to assess the performance of component lifetimes. Using a documented field survey protocol, 19 field surveys were conducted. The focus of this survey strategy is to investigate spatial continuity in degradation modes. By combining field survey data with real-time satellite weather data, stressor / response models have been trained. Generalized additive Models (GAM) model was created to predict the value of degradation based on measured predictors. Two different GAM constructions were testing using different implementations of basis splines. The model includes variables on the environmental stressors of the system and the location of each measurement in the PV mounting structure. The incorporation of hierarchical structure into the models allowed for material specific degradation rates, while maintaining the assumption of a global trend. The model performed well with an adjusted R2 of 0.975 for yellowness index prediction.

backsheet↗

Degradation in Photovoltaic Encapsulant Transmittance: Results of the Second PVQAT TG5 Artificial Weathering Study

The optical degradation of encapsulants from ultraviolet (UV) radiation has historically resulted in a significant loss in performance throughout the life of a photovoltaic (PV) module. IEC test methods have recently been developed to screen for PV encapsulants prone to loss in optical performance. The present study was performed to benchmark polymeric packaging materials relative to IEC 62788-1-4 (covering the measurement of optical transmittance) and IEC 62788-1-7 (on the durability of transmittance), provide feedback toward improvement of the methods, and develop insight regarding optical degradation. Contemporary materials were examined, including: poly(ethylene-co-vinyl acetate) (EVA), thermoplastic polyolefin (TPO), polyolefin elastomer (POE), and polyvinyl butyral (PVB) encapsulants; a poly(ethene-co-tetrafluoroethene)/poly(ethylene terephthalate) (ETFE/PET) transparent backsheet; and a polystyrene (PS) working reference material. The use of silica-, specialty-, and rolled-glass was also compared in laminated coupons. Specimen size was separately examined from 2.5 cm to 12.5 cm. Weathering was performed with a xenon source, using IEC TS 62788-7-2 methods A2, A3, A4, and A5 (chamber temperature of 55 Degrees C, 65 Degrees C, 75 Degrees C, or 85 Degrees C), respectively. Characterizations were made using a UV-VIS-NIR spectrophotometer (transmittance and reflectance, with and without an integrating sphere), a UV-VIS fluorescence spectrophotometer, a camera, and an optical microscope. Performance was analyzed, including solar weighted transmittance, yellowness index, UV cut-off wavelength, and haze (scattering). Separate Arrhenius analyses were performed to assess retention of transmittance and changes in yellowness index. The activation energy for both characteristics was found to range from 15 kJ?mol-1-80 kJ/mol-1, with an average of 48 kJ/mol-1, similar to the average of 45 kJ/mol-1 identified in the previous international PV Quality Assurance Task Force (PVQAT) Task Group 5 (TG5) study of more traditional encapsulants. The separate degradation modes of discoloration and scattering were distinguished in the encapsulants using a comprehensive spectral characterization. Based on these results, the IEC 62788-1-7 pass/fail criteria of 5% change in transmittance was confirmed to identify a known bad encapsulant.

durability↗

Advances in statistical methods for cancer surveillance research: an age-period-cohort perspective

Background: Analysis of Lexis diagrams (population-based cancer incidence and mortality rates indexed by age group and calendar period) requires specialized statistical methods. However, existing methods have limitations that can now be overcome using new approaches. Methods: We assembled a “toolbox” of novel methods to identify trends and patterns by age group, calendar period, and birth cohort. We evaluated operating characteristics across 152 cancer incidence Lexis diagrams compiled from United States (US) Surveillance, Epidemiology and End Results Program data for 21 leading cancers in men and women in four race and ethnicity groups (the “cancer incidence panel”). Results: Nonparametric singular values adaptive kernel filtration (SIFT) decreased the estimated root mean squared error by 90% across the cancer incidence panel. A novel method for semi-parametric age-period-cohort analysis (SAGE) provided optimally smoothed estimates of age-period-cohort (APC) estimable functions and stabilized estimates of lack-of-fit (LOF). SAGE identified statistically significant birth cohort effects across the entire cancer panel; LOF had little impact. As illustrated for colon cancer, newly developed methods for comparative age-period-cohort analysis can elucidate cancer heterogeneity that would otherwise be difficult or impossible to discern using standard methods. Conclusions: Cancer surveillance researchers can now identify fine-scale temporal signals with unprecedented accuracy and elucidate cancer heterogeneity with unprecedented specificity. Birth cohort effects are ubiquitous modulators of cancer incidence in the US. The novel methods described here can advance cancer surveillance research.

60 APPLIED LIFE SCIENCES↗

Accumulator Sizing for the Hybrid Hydraulic Electric Architecture (HHEA) Using Dynamic Programming

Abstract The Hybrid Hydraulic Electric Architecture (HHEA) is an approach to electrifying and improving that does not require very large electric machines. The majority power is provided hydraulically via a set of common pressure rails while small electric machines are used to modulate that power to meet exact demand. Each common pressure rail has an accumulator, which keeps the pressure of each rail near constant as long as the volume in the accumulator does not change much. A main pump adds or removes fluid from each accumulator as needed to reduce volume changes in the accumulators. It is the operation of this main pump which is investigated here. In previous analysis it was assumed that the size of the accumulators was large enough to account for any difference in fluid volume without significantly changing the pressure. In this paper, the required accumulator sizes are studied using a dynamic programming approach. At each time step the inlet and outlet of the main pump could be connected to any of the common pressure rails. These decisions for the main pump were optimized to minimize the required accumulator size. Case studies are conducted using drive cycles from a 22-ton excavator, a 20-ton wheel loader and a 5-ton excavator. The machines could be operated with total accumulator sizes of less than 20, 10, and 5 liters respectively. Required accumulator size was found to vary with pump flow rate, the frequency at which the pump/motor was able to change operating conditions, the tolerance level of pressure deviation and the accumulator’s polytropic index.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Inserting a Low-Refractive-Index Dielectric Rear Reflector into PERC Cells: Challenges and Opportunities

One of the simplest and most effective ways to reduce the operating temperature of photovoltaic systems in the field is to reflect unusable, 'sub-bandgap' light with energies below the cell absorber's bandgap energy. In this work, low-refractive index SiO2 nanoparticle films inserted between c-Si wafers and metal electrodes significantly increase the reflectance of sub-bandgap light in fabricated test structures. These films are then integrated into the standard PERC fabrication sequence, revealing that the reflection benefits remains after some processing steps but are lost in certain conditions. The SiO2 nanoparticle films are easily ablated during laser contact opening, but are undesirably etched during post-laser cleaning. After Al metallization, the fully fabricated devices show enhanced sub-bandgap reflection when annealed at moderate temperatures, but energy-dispersive X-ray spectroscopy shows that high-temperature firing causes unwanted Al penetration into the SiO2 nanoparticle film, compromising reflection.

14 SOLAR ENERGY↗

Seven days of ischemic preconditioning augments hypoxic exercise ventilation and muscle oxygenation in recreationally trained males

This investigation sought to assess whether single or repeated bouts of ischemic preconditioning (IPC) could improve oxyhemoglobin saturation ([Formula: see text]) and/or attenuate reductions in muscle tissue saturation index (TSI) during submaximal hypoxic exercise. Fifteen healthy young men completed submaximal graded exercise under four experimental conditions: 1) normoxia (NORM), 2) hypoxia (HYP) [oxygen fraction of inspired air ([Formula: see text]) = 0.14, ∼3,200 m], 3) hypoxia preceded by a single session of IPC (IPC1-HYP), and 4) hypoxia preceded by seven sessions of IPC, one a day for 7 consecutive days (IPC7-HYP). IPC7-HYP heightened minute ventilation (V̇e) at 80% HYP peak cycling power output ( W peak ) (+10.47 ± 3.35 L·min −1 , P = 0.006), compared with HYP, as a function of increased breathing frequency. Both IPC1-HYP (+0.17 ± 0.04 L·min −1 , P < 0.001) and IPC7-HYP (+0.16 ± 0.04 L·min −1 , P < 0.001) elicited greater oxygen consumption (V̇o 2 ) across exercise intensities compared with NORM, whereas V̇o 2 was unchanged with HYP alone. [Formula: see text] was unchanged by either IPC condition at any exercise intensity, yet the reduction of muscle TSI during resting hypoxic exposure was attenuated by IPC7-HYP (+9.9 ± 3.6%, P = 0.040) compared with HYP, likely as a function of reduced local oxygen extraction. Considering all exercise intensities, IPC7-HYP attenuated reductions of TSI with HYP (+6.4 ± 1.8%, P = 0.001). Seven days of IPC heightens ventilation, posing a threat to ventilatory efficiency, during high-intensity submaximal hypoxic exercise and attenuates reductions in hypoxic resting and exercise muscle oxygenation in healthy young men. A single session of IPC may be capable of modulating hypoxic ventilation; however, our present population was unable to demonstrate this with certainty.

Physiology↗

New principles of self‐organization created through the interplay of DNA condensates, microtubules, and motors

Bioinspired design—which holds great promise for a new generation of materials that are robust to defects, scalable under green manufacture, environmentally responsive, and programmably reconfigurable—requires mastery over molecular self-organization. Yet, from its specific mechanisms to most general architectures, the principles governing self-organization remain poorly understood and not even fully enumerated. For living systems, one obvious architectural principle is the modular reuse of a few simple molecular components in myriad combinations to achieve more complex phenomena. For example, the mechanical tasks of a cell are driven by the nonequilibrium dynamics of cytoskeletal filaments and molecular motors—the same filaments and motors, reprogrammed by a variety of modulators, perform tasks ranging from cell movement to division. Similarly, many compartmentalization tasks are performed by liquid-like condensates of simple components, which act as membraneless organelles to localize particular molecules in space and time (e.g. for gene regulation or RNA processing). In a few cases, condensates combine and interact with the cytoskeleton to create still more complex phenomena, e.g. the nucleation of microtubule asters from the centrosome (a protein condensate) to form the mitotic spindle during cell division. Very little is known about the fundamental mechanisms of such filament-plus-condensate phenomena. Despite few examples, the landscape of behaviors that can be achieved through the combination of condensates, filaments, and motors appears vast. However, exploration has been hindered by a lack of systems that have sufficiently programmable and dynamically tunable interactions between component condensates, filaments, and motors. We proposed to combine programmable DNA condensates, filamentous microtubules, and light-controlled motors into self-organizing systems whose principles go beyond those that have been observed in nature. In one limit, our systems will use microtubules and motors to create the molecular analog of a network of roads, which will organize droplets of DNA condensates capable of carrying molecular cargo. DNA condensates coupled to motors will flow from one microtubule aster hub to another, with their direction and timing controlled by DNA circuits. In another limit, microtubules will swim through bulk DNA condensates and exhibit strong interactions with boundaries between different types of condensates. Microtubule swimmers will reflect, get trapped, or refract at boundaries, under a mechanical analog of the classical optical index of refraction. DNA condensates having different mechanical indexes of refraction will be used to construct the analog of optical lenses, so that microtubule swimmers can be manipulated like light—collimated, diffracted, focused, and sorted based on properties analogous to wavelength. These two limits define two new architectures, within which multiple new mechanistic principles for self-organization will be discovered and explored. To explore these architectures, the motor-based coupling between DNA condensates and filaments will be controlled in time and space through the use of opto-proteins that create reversible links between DNA condensates and motors upon illumination. For each principle of interest, patterns of light will create virtual experiments by defining patterns of activity where DNA condensates walk along filaments, or filaments swim through condensates, and patterns of inactivity which will serve either as controls, or as boundary conditions vital to create the desired phenomena. This research serves the goals of Basic Energy Sciences Biomolecular Material Program by elucidating the principles by which the emergent, nonequilibrium behavior of collections of DNA condensates, motors, and microtubules can be programmed by environmental light patterns to create complex motion and materials transport. Because DNA condensates can be readily coupled to virtually any high performance nanomaterial, from carbon nanotubes, to metal nanoparticles, to light harvesting systems, this work provides a path to the construction, self-maintenance and reconfiguration of materials relevant to the Department of Energy.

60 APPLIED LIFE SCIENCES↗

DISCo-microbe: design of an identifiable synthetic community of microbes

Microbiomes are extremely important for their host organisms, providing many vital functions and extending their hosts’ phenotypes. Natural studies of host-associated microbiomes can be difficult to interpret due to the high complexity of microbial communities, which hinders our ability to track and identify individual members along with the many factors that structure or perturb those communities. For this reason, researchers have turned to synthetic or constructed communities in which the identities of all members are known. However, due to the lack of tracking methods and the difficulty of creating a more diverse and identifiable community that can be distinguished through next-generation sequencing, most such in vivo studies have used only a few strains. To address this issue, we developed DISCo-microbe, a program for the design of an identifiable synthetic community of microbes for use in in vivo experimentation. The program is composed of two modules; (1) create, which allows the user to generate a highly diverse community list from an input DNA sequence alignment using a custom nucleotide distance algorithm, and (2) subsample, which subsamples the community list to either represent a number of grouping variables, including taxonomic proportions, or to reach a user-specified maximum number of community members. As an example, we demonstrate the generation of a synthetic microbial community that can be distinguished through amplicon sequencing. The synthetic microbial community in this example consisted of 2,122 members from a starting DNA sequence alignment of 10,000 16S rRNA sequences from the Ribosomal Database Project. We generated simulated Illumina sequencing data from the constructed community and demonstrate that DISCo-microbe is capable of designing diverse communities with members distinguishable by amplicon sequencing. Using the simulated data we were able to recover sequences from between 97–100% of community members using two different post-processing workflows. Furthermore, 97–99% of sequences were assigned to a community member with zero sequences being misidentified. We then subsampled the community list using taxonomic proportions to mimic a natural plant host–associated microbiome, ultimately yielding a diverse community of 784 members. DISCo-microbe can create a highly diverse community list of microbes that can be distinguished through 16S rRNA gene sequencing, and has the ability to subsample (i.e., design) the community for the desired number of members and taxonomic proportions. Although developed for bacteria, the program allows for any alignment input from any taxonomic group, making it broadly applicable. The software and data are freely available from GitHub and Python Package Index (PYPI).

16S rRNA↗

Reducing Uncertainty of Fielded Photovoltaic Performance (Final Technical Report)

Improved analysis and reporting of photovoltaic (PV) field performance increases the certainty of owners and financiers that systems will perform as expected. Advanced module technologies (e.g., PERC, HJT, and bifacial) introduce new degradation mechanisms and performance characteristics. The FY19-21 Reducing Uncertainty project leveraged data from the ever-increasing PV fleet to develop models and understanding of the field performance of existing and new technologies. Specifically, we accomplished: report on field performance and degradation rates for high-efficiency silicon (HJT, PERC, IBC) and more conventional technologies; developed automated analysis techniques to quantify system performance (performance ratio, energy yield) and production shortfalls (soiling, degradation, availability); refined the RdTools software toolkit to bring standard, validated analysis techniques to bear on third-party data; analyzed and reported on large datasets including Treasury data and Lawrence Berkeley National Laboratory's Utility-Scale dataset to expand the high-quality degradation-rate histogram published previously; worked with industry partners and the DuraMAT data hub to enable private parties to share and aggregate PV production data anonymously, leveraging cloud-based data analysis infrastructure and publishing on US fleet-scale performance comprising over 7GW of operating systems. (https://www.nrel.gov/pv/fleet-performance-data-initiative.html). Through our industry collaborations we have engaged in NDA-covered data transfer with twelve PV fleet owners as of January 2022, with more agreements in negotiation. Our scalable cloud-based time series database contains over 30 billion rows (20TB) of PV time series data, representing over 1700 commercial and utility-scale systems, and over 7.2 GW of DC capacity (Fig 1). Initial field performance results have been distributed in several public reports. Because our fleet composition and data quality methods are continually improving, annual updates to these results are published to our PV Fleet webpage [ https://www.nrel.gov/pv/fleet-performance-data-initiative.html ] and DuraMAT data hub [DOI: 10.21948/1842958]. Another existing dissemination channel used for observed soiling losses is a map we maintain for soiling losses. Additional products developed include a report detailing fleet-wide performance index, availability, startup loss and snow loss factors, a detailed report on the 1603 grant dataset comprising over 100,000 PV systems with failure and performance details and a utility-scale report coauthored with LBNL on 31 GW of system performance.

14 SOLAR ENERGY↗