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Rhombohedral bismuth oxide has emerged as a promising SOFC electrolyte material, particularly at low-to-intermediate temperatures. The stability window of this phase was explored using a co-doped system, using yttrium and lanthanum or aluminum, over 7.5–25 % total dopant content and 0.907–1.154 Å weighted average dopant cationic radius. The phase transformation behavior of rhombohedral-rich phase mixtures was also investigated in situ from 360 to 656 °C to assess the effects of minor impurity phases and thermal evolution pathways. Our results reveal that low-dopant, rhombohedral-rich compositions exhibit poor structural stability upon heating. Anisotropic thermal expansion behavior was observed over 450–656 °C, serving as a sensitive indicator of sequential phase changes, including the rhombohedral β2-to-β1 transition (∼450 °C), monoclinic phase formation, and delayed or incomplete formation of the cubic phase (∼ 550 °C). These multiple unfavorable phase transitions compromise the mechanical robustness required for SOFC operation. This study underscores the need for compositional tuning to balance ionic conductivity with thermal phase stability in rhombohedral Bi 2 O 3 -based systems.
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Single quadrupole mass spectrometry (MS) with enhanced in-source multiple fragment ion monitoring was designed to perform high sensitivity quantitative mass analyses. Enhanced in-source fragmentation amplifies fragmentation from traditional soft electrospray ionization producing fragment ions that have been found to be identical to those generated in tandem MS. We have combined enhanced in-source fragmentation data with criteria established by the European Union Commission Directive 2002/657/EC for electron ionization single quadrupole quantitative analysis to perform quantitative analyses. These experiments were performed on multiple types of complex samples that included a mixture of 50 standards, as well as cell and plasma extracts. The dynamic range for these quantitative analyses was comparable to triple quadrupole multiple reaction monitoring (MRM) analyses at up to 5 orders of magnitude with the cell and plasma extracts showing similar matrix effects across both platforms. Amino acid and fatty acid measurements performed from certified NIST 1950 plasma with isotopically labeled standards demonstrated accuracy in the range of 91–110% for the amino acids, 76–129% for the fatty acids, and good precision (coefficient of variation <10%). To enhance specificity, a newly developed correlated ion monitoring algorithm was designed to facilitate these analyses. This algorithm autonomously processes, aligns, filters, and compiles multiple ions within one chromatogram enabling both precursor and in-source fragment ions to be correlated within a single chromatogram, also enabling the detection of coeluting species based on precursor and fragment ion ratios. Single quadrupole instrumentation can provide MRM level quantitative performance by monitoring/correlating precursor and fragment ions facilitating high sensitivity analysis on existing single quadrupole instrumentation that are generally inexpensive, easy to operate, and technically less complex.
Direct lithium extraction via membrane separations has been fundamentally limited by lack of monovalent ion selectivity exhibited by conventional polymeric membranes, particularly between sodium and lithium ions. Recently, a 12- Crown-4-functionalized polynorbornene membrane was shown to have the largest lithium/sodium permeability selectivity observed in a fully aqueous system to date. Using atomistic molecular dynamics simulations, we reveal that this selectivity is due to strong interactions between sodium ions and 12-Crown-4 moieties, which reduce sodium ion diffusivity while leaving lithium ion mobility relatively unaffected. Furthermore, the ion diffusivities in the membrane, when scaled by their respective solution diffusivities and free ion fractions, can be collapsed to an almost universal relationship depending on solvent volume fraction.
The metastable purple [(Py 5 Me 2 )Ru III (N 3 )] 2+ ion reacts with PPh 3 at room temperature to form the phosphinimine complex [(Py 5 Me 2 )Ru II (N(H)PPh 3 )] 2+ and free [H 2 NPPh 3 ] + in a combined 23% conversion. Here, mechanistic studies suggest that this is the first metallo-Staudinger reaction of a late transition metal that bypasses the nitrido mechanism and instead utilizes a Ru–N=N=N–PPh 3 phosphazide intermediate.
A novel multi-electrode glow discharge provides increased sensitivity and analytical flexibility for both atomic and molecular MS determinations.
High-resolution synchrotron X-ray diffraction data confirm the stabilization of the average structure at ambient temperatures as a defect fluorite structure in ternary and quaternary oxides of Bi 2 O 3 , where some Bi cations are replaced with Dy 3+ and/or Er 3+ , Nb 5+ and W 6+ . Rietveld refinement of the diffraction data indicates that when the larger cation Er 3+ (0.89 Å) is replaced with smaller cations W 6+ (0.42 Å) and/or Nb 5+ (0.48 Å), the unit-cell parameter a, based on the relative ionic radii of the substituents, counterintuitively increases. Total scattering data show that the identity of the cation substituent is important in determining the positions of some Bi cations in the defect fluorite structure. Er 3+ and Dy 3+ cations induce monoclinic-like distortions that extend only locally in the Bi 3+ sublattice. X-ray absorption spectroscopy provides evidence that some Dy cations, in the δ phases, prefer to keep a similar local environment to that in the parent Dy 2 O 3 . W cations are found to be predominantly tetrahedrally coordinated in the δ phases explored in this work.
Training neural networks for neuromorphic deployment is non-trivial. There have been a variety of approaches proposed to adapt back-propagation or back-propagation-like algorithms appropriate for training. Considering that these networks often have very different performance characteristics than traditional neural networks, it is often unclear how to set either the network topology or the hyperparameters to achieve optimal performance. In this work, we introduce a Bayesian approach for optimizing the hyperparameters of an algorithm for training binary communication networks that can be deployed to neuromorphic hardware. We show that by optimizing the hyperparameters on this algorithm for each dataset, we can achieve improvements in accuracy over the previous state-of-the-art for this algorithm on each dataset (by up to 15 percent). This jump in performance continues to emphasize the potential when converting traditional neural networks to binary communication applicable to neuromorphic hardware.
This project explores the use of femto-second (fs) lasers to form glass-to-glass welds for hermetically sealed, polymer-free solar modules. Low iron solar glass coupons were welded together without the use of glass filler using a fs laser with dedicated optics to elongate the focal plane parallel to the incident beam. The resulting welds were then stress tested to failure to reveal the critical stress intensity factor, KIc. These values were used in a structural mechanics model of a 1 m by 2 m glass/glass module under a simulated static load test. The results show that the fs laser welds are strong enough for a suitably framed module to pass the IEC 61215 static load test with a load of 5400 Pa. Key to this finding is that the module must be framed and braced, and the glass must be ribbed to allow pockets for the cells and welds inside the border of the module. The result is a module design that is completely polymer-free, hermetically sealed, has improved thermal properties, and is easily recycled.
We present results for a 75-kW field array deployed with rows of 5 different commercially available bifacial technologies. Four PERC (multi and mono) and 1 Silicon Heterojunction manufacturers are represented. Reference strings of equivalent monofacial PV modules are also installed in the 10-row field. High accuracy string-level DC monitoring and module-level measurements have been recorded for six months. Analysis indicates performance within expectation, with a cumulative model mean error within +/- 2% for both bifacial and monofacial models and cumulative bifacial energy gain between 6-9%. A custom-module to measure shading loss from the torque-tube was installed. One month data shows up to 6% irradiance non-uniformity. A fixed-tilt test-site with 3 years of bifacial data is also presented, with a bifacial gain of 3%.
We investigate the impact of spectral dependence of ground surface reflectivity on albedo and rearside irradiance measurements necessary for bifacial photovoltaic (PV) module performance estimation and monitoring. Because PV modules are spectrally selective, albedo and irradiance measurements performed with common irradiance sensors may require spectral mismatch corrections when used for performance prediction. We investigate via simulation the differences in spectrally responsive albedo measured with thermopile pyranometers and crystalline silicon PV reference cells in comparison to a typical crystalline-silicon bifacial PV module. Simulations are performed for nine different representative ground surface materials using simulated solar spectra together with spectral reflectivity data distributed with the SMARTS simulation software. For the materials considered, the results show that albedo spectral mismatch relative to the bifacial module is distributed over a range of ±9.2% for thermopile pyranometers versus only ±3.7% for a typical PV reference cell. We consider the impact of this spectrally-responsive albedo mismatch on bifacial PV module rearside irradiance measurements. Using synthesized rearside spectral irradiance distributions, we find that for the nine different ground surface materials the predicted rearside irradiance measurement deviates from the effective irradiance observed by the PV module by on the order of 16.5 W/m 2 for the pyranometer and 3.6 W/m 2 for the PV reference cell. We discuss the implications for bifacial albedo and irradiance measurement.
Neuromorphic computing has several characteristics that make it an extremely compelling computing paradigm for post Moore computation. Some of these characteristics include intrinsic parallelism, inherent scalability, collocated processing and memory, and event-driven computation. While these characteristics impart energy efficiency to neuromorphic systems, they do come with their own set of challenges. One of the biggest challenges in neuromorphic computing is to establish the theoretical underpinnings of the computational complexity of neuromorphic algorithms. In this paper, we take the first steps towards defining the space and time complexity of neuromorphic algorithms. Specifically, we describe a model of neuromorphic computation and state the assumptions that govern the computational complexity of neuromorphic algorithms. Next, we present a theoretical framework to define the computational complexity of a neuromorphic algorithm. We explicitly define what space and time complexities mean in the context of neuromorphic algorithms based on our model of neuromorphic computation. Finally, we leverage our approach and define the computational complexities of six neuromorphic algorithms: constant function, successor function, predecessor function, projection function, neuromorphic sorting algorithm and neighborhood subgraph extraction algorithm.
The Energy Sciences Network (ESnet) is the high-performance network user facility for the US Department of Energy (DOE) Office of Science (SC) and delivers highly reliable data transport capabilities optimized for the requirements of data-intensive science. In essence, ESnet is the circulatory system that enables the DOE science mission by connecting all of its laboratories and facilities in the US and abroad. ESnet is funded and stewarded by the Advanced Scientific Computing Research (ASCR) program and managed and operated by the Scientific Networking Division at Lawrence Berkeley National Laboratory (LBNL). ESnet is widely regarded as a global leader in the research and education networking community. Throughout 2021, ESnet and the Office of Fusion Energy Sciences (FES) of the DOE SC organized an ESnet requirements review of FES-supported activities. Preparation for these events included identification of key stakeholders: program and facility management, research groups, and technology providers. Each stakeholder group was asked to prepare formal case study documents about their relationship to the FES program to build a complete understanding of the current, near-term, and long-term status, expectations, and processes that will support the science going forward.
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