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Wavefront errors of rotationally parabolic refractive X-ray lenses made of beryllium (Be CRLs) have been recovered for various lens sets and X-ray beam configurations. Due to manufacturing via an embossing process, aberrations of individual lenses within the investigated ensemble are very similar. By deriving a mean single-lens deformation for the ensemble, aberrations of any arbitrary lens stack can be predicted from the ensemble with σ¯ = 0.034λ. Using these findings the expected focusing performance of current Be CRLs are modeled for relevant X-ray energies and bandwidths and it is shown that a correction of aberrations can be realised without prior lens characterization but simply based on the derived lens deformation. As a result, the performance of aberration-corrected Be CRLs is discussed and the applicability of aberration-correction demonstrated over wide X-ray energy ranges.
A theory for the nuclear chemical shifts of molecules in arbitrary spin states is applied to a set of paramagnetic organometallic complexes of 3d metals. Ligand chemical shifts are calculated and analyzed using Kohn–Sham (KS) density functional theory with and without relativistic corrections. The roles of the KS delocalization error, Gaussian-type versus Slater-type basis sets, relativistic effects (scalar and spin–orbit), and zero field splitting (ZFS) are investigated. A strong functional dependence of the chemical shifts is apparent and correlated with the delocalization error. Here, the functional dependence is between one and two orders of magnitude larger than variations of the NMR shifts due the other influences that are investigated. ZFS effects are negligible in the determination of the NMR chemical shifts of the complexes except at very low temperatures. The DFT calculated shifts agree reasonably well with experiment. A 73 ppm difference in the NMR shifts of the two protons in the amide groups of a high-spin Fe(II) macrocycle complex arises from selective O → Fe dative bonding that only involves the transfer of β spin density, along with orbital delocalization throughout the ligand bonding framework which electronically couples the coordinating oxygen lone pair orbitals directly to the amide trans proton.
Purpose: An adaptive proton therapy workflow using cone beam computed tomography (CBCT) is proposed. It consists of an online evaluation of a fast range-corrected dose distribution based on a virtual CT (vCT) scan. This can be followed by more accurate offline dose recalculation on the vCT scan, which can trigger a rescan CT (rCT) for replanning. Methods and Materials: The workflow was tested retrospectively for 20 consecutive lung cancer patients. A diffeomorphic Morphon algorithm was used to generate the lung vCT by deforming the average planning CT onto the CBCT scan. An additional correction step was applied to account for anatomic modifications that cannot be modeled by deformation alone. A set of clinical indicators for replanning were generated according to the water equivalent thickness (WET) and dose statistics and compared with those obtained on the rCT scan. The fast dose approximation consisted of warping the initial planned dose onto the vCT scan according to the changes in WET. The potential under- and over-ranges were assessed as a variation in WET at the target's distal surface. Results: The range-corrected dose from the vCT scan reproduced clinical indicators similar to those of the rCT scan. The workflow performed well under different clinical scenarios, including atelectasis, lung reinflation, and different types of tumor response. Between the vCT and rCT scans, we found a difference in the measured 95% percentile of the over-range distribution of 3.4 ± 2.7 mm. The limitations of the technique consisted of inherent uncertainties in deformable registration and the drawbacks of CBCT imaging. The correction step was adequate when gross errors occurred but could not recover subtle anatomic or density changes in tumors with complex topology. Conclusions: A proton therapy workflow based on CBCT provided clinical indicators similar to those using rCT for patients with lung cancer with considerable anatomic changes.
The efficient solution of sparse, linear systems that arise through the discretization of partial differential equations remains a key challenge for a range of high performance scientific simulations. One approach for reducing data movement and improving performance is by exposing and exploiting structure in a problem through the use of robust structured multilevel solvers. By choosing coarsening that preserves the structure of the problem, these methods maintain efficient structured computation and communication throughout the multigrid hierarchy. However, when coarsening is not permitted to be dependent on the operator, anisotropy must be addressed by the smoother — producing error compatible for coarse-grid correction with structured coarsening. Here, the components required in a scalable parallel structured solver are described with a focus on memory and communication efficiency of robust smoothers. While the implementation of communication and memory reduction techniques in smoothers integrated in a complete 3D solver present a significant engineering challenge, a novel approach is proposed that addresses these challenges systematically through a change to the solver’s execution model. Enabled by user-level threading paired with a set of data and communication abstractions, this approach permits seamless aggregation of communication in plane smoothers — directly reusing code for a 2D distributed multilevel cycle. Results show an effective reduction in communication costs for coarse-grid problems, and result in a speedup of 8.7x in smoothing routines shown in Fig. 12 using this approach. This produces a significant improvement to strong scalability while maintaining favorable weak scaling behavior. Finally, a parallel scaling study using a series of refined meshes is included that demonstrates the effectiveness of this approach in an application of interest.
We present the DEC-RIMP2-F12 method where we have augmented the Divide Expand-Consolidate resolution-of-the-identity second-order Møller-Plesset perturbation theory method (DEC-RIMP2) [P. Baudin et al., J. Chem. Phys. 144, 054102 (2016)] with an explicitly correlated (F12) correction. Here, the new method is linear-scaling, massively parallel, and it corrects for the basis set incompleteness error in an efficient manner. In addition, we observe that the F12 contribution decreases the domain error of the DEC-RIMP2 correlation energy by roughly an order of magnitude. An important feature of the DEC scheme is the inherent error control defined by a single parameter, and this feature is also retained for the DEC-RIMP2-F12 method. In this paper we present the working equations for the DEC-RIMP2-F12 method and proof of concept numerical results for a set of test molecules.
The electrochemical CO 2 reduction reaction (CO 2 RR) powered by excess zero-carbon-emission electricity to produce especially multicarbon (C 2+ ) products could contribute to a carbon-neutral to carbon-negative economy. Foundational to the rational design of efficient, selective CO 2 RR electrocatalysts is mechanistic analysis of the best metal catalyst thus far identified, namely, copper (Cu), via quantum mechanical computations to complement experiments. Here, we apply embedded correlated wavefunction (ECW) theory, which regionally corrects the electron exchange-correlation error in density functional theory (DFT) approximations, to examine multiple C–C coupling steps involving adsorbed CO (*CO) and its hydrogenated derivatives on the most ubiquitous facet, Cu(111). We predict that two adsorbed hydrogenated CO species, either *COH or *CHO, are necessary precursors for C–C bond formation. The three kinetically feasible pathways involving these species yield all three possible products: *COH–CHO, *COH–*COH, and *OCH–*OCH. The most kinetically favorable path forms *COH–CHO. In contrast, standard DFT approximations arrive at qualitatively different conclusions, namely, that only *CO and *COH will prevail on the surface and their C–C coupling paths produce only *COH–*COH and *CO–*CO, with a preference for the first product. This work demonstrates the importance of applying qualitatively and quantitatively accurate quantum mechanical method to simulate electrochemistry in order ultimately to shed light on ways to enhance selectivity toward C 2+ product formation via CO 2 RR electrocatalysts.
Optical atomic clocks with unrivaled precision and accuracy have advanced the frontier of precision measurement science and opened new avenues for exploring fundamental physics. A fundamental limitation on clock precision is the standard quantum limit (SQL), which stems from the uncorrelated projection noise of each atom. State-of-the-art optical lattice clocks interrogate large ensembles to minimize the SQL, but density-dependent frequency shifts pose challenges to scaling the atom number. The SQL can be surpassed, however, by leveraging entanglement, though it remains an open problem to achieve quantum advantage from spin squeezing at state-of-the-art stability levels. Here, we demonstrate clock performance beyond the SQL, achieving a fractional frequency precision of 1.1 × 10 −18 for a single spin-squeezed clock. With cavity-based quantum nondemolition measurements, we prepare two spin-squeezed ensembles of ∼30 000 strontium atoms confined in a two-dimensional optical lattice. A synchronous clock comparison with an interrogation time of 61 ms achieves a metrological improvement of 2.0(2) dB beyond the SQL, after correcting for state preparation and measurement errors. These results establish the most precise entanglement-enhanced clock to date and offer a powerful platform for exploring the interplay of gravity and quantum entanglement.
Quantum key distribution (QKD) is a quantum-proof key-exchange scheme which is fast approaching the communication industry. An essential component in QKD is the information reconciliation step, which is used for correcting the quantum-channel noise errors. The recently suggested blind-reconciliation technique, based on low-density parity-check codes, offers remarkable prospectives for efficient information reconciliation without an a priori quantum bit error rate estimation. We suggest an improvement of the blind-information-reconciliation protocol promoting a significant increase in the efficiency of the procedure and reducing its interactivity. Finally, the proposed technique is based on introducing symmetry in operations of parties, and the consideration of results of unsuccessful belief-propagation decodings.
We perform a new analysis of electron-proton scattering data to determine the proton electric and magnetic radii, enforcing model-independent constraints from form factor analyticity. A wide-ranging study of possible systematic effects is performed. An improved analysis is developed that rebins data taken at identical kinematic settings and avoids a scaling assumption of systematic errors with statistical errors. Employing standard models for radiative corrections, our improved analysis of the 2010 Mainz A1 Collaboration data yields a proton electric radius r E = 0.895(20) fm and magnetic radius r M = 0.776(38) fm. A similar analysis applied to world data (excluding Mainz data) implies r E = 0.916(24) fm and r M = 0.914(35) fm. The Mainz and world values of the charge radius are consistent, and a simple combination yields a value r E = 0.904(15) fm that is 4σ larger than the CREMA Collaboration muonic hydrogen determination. The Mainz and world values of the magnetic radius differ by 2.7σ, and a simple average yields r M = 0.851(26) fm. As a result, the circumstances under which published muonic hydrogen and electron scattering data could be reconciled are discussed, including a possible deficiency in the standard radiative correction model which requires further analysis.
Event-mode data collection presents remarkable new opportunities for time-of-flight neutron scattering studies of collective excitations, diffuse scattering from short-range atomic and magnetic structures, and neutron crystallography. In these experiments, large volumes of the reciprocal space are surveyed, often using different wavelengths and counting times. These data then have to be added together, with accurate propagation of the counting errors. This paper presents a statistically correct way of adding and histogramming the data for single-crystal time-of-flight neutron scattering measurements. In order to gain a broader community acceptance, particular attention is given to improving the efficiency of calculations.
AutoCAD, Zemax Optic Studio 15, and Interactive Data Language (IDL) with the Proper Library are used to computationally model and test a diffractive mask (DiM) suitable for use in the Gemini Multi-Conjugate Adaptive Optics System (GeMS) on the Gemini South Telescope. Systematic errors in telescope imagery are produced when the light travels through the adaptive optics system of the telescope. DiM is a transparent, flat optic with a pattern of miniscule dots lithographically applied to it. It is added ahead of the adaptive optics system in the telescope in order to produce diffraction spots that will encode systematic errors in the optics after it. Once these errors are encoded, they can be corrected for. DiM will allow for more accurate measurements in astrometry and thus improve exoplanet detection. Furthermore, the mechanics and physical attributes of the DiM are modeled in AutoCAD. Zemax models the ray propagation of point sources of light through the telescope. IDL and Proper simulate the wavefront and image results of the telescope. Aberrations are added to the Zemax and IDL models to test how the diffraction spots from the DiM change in the final images. Based on the Zemax and IDL results, the diffraction spots are able to encode the systematic aberrations.
ABSTRACT GapMind is a Web-based tool for annotating amino acid biosynthesis in bacteria and archaea ( http://papers.genomics.lbl.gov/gaps ). GapMind incorporates many variant pathways and 130 different reactions, and it analyzes a genome in just 15 s. To avoid error-prone transitive annotations, GapMind relies primarily on a database of experimentally characterized proteins. GapMind correctly handles fusion proteins and split proteins, which often cause errors for best-hit approaches. To improve GapMind’s coverage, we examined genetic data from 35 bacteria that grow in defined media without amino acids, and we filled many gaps in amino acid biosynthesis pathways. For example, we identified additional genes for arginine synthesis with succinylated intermediates in Bacteroides thetaiotaomicron , and we propose that Dyella japonica synthesizes tyrosine from phenylalanine. Nevertheless, for many bacteria and archaea that grow in minimal media, genes for some steps still cannot be identified. To help interpret potential gaps, GapMind checks if they match known gaps in related microbes that can grow in minimal media. GapMind should aid the identification of microbial growth requirements. IMPORTANCE Many microbes can make all of the amino acids (the building blocks of proteins). In principle, we should be able to predict which amino acids a microbe can make, and which it requires as nutrients, by checking its genome sequence for all of the necessary genes. However, in practice, it is difficult to check for all of the alternative pathways. Furthermore, new pathways and enzymes are still being discovered. We built an automated tool, GapMind, to annotate amino acid biosynthesis in bacterial and archaeal genomes. We used GapMind to list gaps: cases where a microbe makes an amino acid but a complete pathway cannot be identified in its genome. We used these gaps, together with data from mutants, to identify new pathways and enzymes. However, for most bacteria and archaea, we still do not know how they can make all of the amino acids.
Technology readiness level (TRL) labeling and forecasting today is done entirely manually. A computationally-assisted model of a TRL-like classification for estimation and tracking could enable a research and development (R&D) portfolio manager to more accurately estimate the relative level of development for technologies in that portfolio. This report describes the work to understand readiness level systems, train a machine learning algorithm to recognize textual indicators of particular readiness levels within written material from carefully chosen case studies in renewable technology development, and apply that algorithm to predict the readiness levels of other technology development cases. We account for the results of each step of the work conducted, including the predictive accuracy of the trained model: 54% correct, within a +/-1 readiness level error threshold and a root mean squared error of 1.88. Great potential remains to improve and apply this model for management of individual projects and across portfolios.
Technology readiness level (TRL) labeling and forecasting today is done entirely manually. A computationally-assisted model of a TRL-like classification for estimation and tracking could enable a research and development (R&D) portfolio manager to more accurately estimate the relative level of development for technologies in that portfolio. This report describes the work to understand readiness level systems, train a machine learning algorithm to recognize textual indicators of particular readiness levels within written material from carefully chosen case studies in renewable technology development, and apply that algorithm to predict the readiness levels of other technology development cases. We account for the results of each step of the work conducted, including the predictive accuracy of the trained model: 54% correct, within a +/-1 readiness level error threshold and a root mean squared error of 1.88. Great potential remains to improve and apply this model for management of individual projects and across portfolios.
This report provides analyses of glass compositions, wash solution compositions, and Product Consistency Test (PCT) leachate compositions for a series of simulated low-activity waste (LAW) glasses fabricated at the Pacific Northwest National Laboratory (PNNL). These data will be used in the development of improved property/composition models for LAW glass at Hanford. Chemical analyses were performed on a representative sample of each of the quenched and sulfur saturated glasses to allow for comparisons with the targeted compositions. For some of the quenched glasses, measured concentrations of Cr 2 O 3 , K 2 O, Na 2 O, P 2 O 5 , and ZrO 2 were below the targeted values. For some of the sulfur saturated glasses, the measured concentrations of Al 2 O 3 , Cl - , Cr 2 O 3 , F - , K 2 O, Na 2 O, P 2 O 5 , V 2 O 5 , and ZrO 2 were generally low relative to the targeted values. A comparison of the measured compositions of the quenched and sulfur saturated versions of the study glasses showed that the measured concentrations of Al 2 O 3 , Cl - , and F - were lower for most of the sulfur saturated glasses. The measured Cr 2 O 3 and K 2 O concentrations were markedly lower for the sulfur saturated versions of the glasses as compared to the quenched versions, which may indicate that these elements partitioned to the soluble sulfur salts. A set of modified glasses was received from PNNL later in this study. Chemical analysis showed that these glasses generally met their targeted compositions. The measured SO 3 concentrations were higher for most of the sulfur saturated glasses relative to those of the quenched versions, as expected. Chemical analysis of the wash solutions that resulted from preparation of the sulfur saturated melts identified Cr, K, Na, P, S, and V as the major elements in solution. With the exception of sulfur, this may explain why the measured concentrations of these components in the study glasses were generally below the targeted values. PNNL performed PCTs on quenched and canister centerline cooled (CCC) versions of the study glasses, as well as the modified glasses. The leachates were sent to SRNL for chemical analysis. Minor scatter in the measured values among the triplicate samples for each glass was noted, with significant differences among the triplicate values noted for four of the CCC glasses. Normalized concentration values for some of the study glasses were greater than the 100 g/L maximum (total dissolution), which may indicate an error in the test. It is recommended that PNNL review the performance of the PCTs to determine the cause of these potential outliers and errors. Revision 1 of this report: Corrects an issue with high chromium measurements for the Mod glass compositions and the second set of PCT leachates, Adds data for the Mod glass wash solutions that were unintentionally omitted, Corrects an issue with rounding of the targeted Al 2 O 3 concentrations in the study glasses
Brown and Mallik and the corresponding corrigendum Brown et al. presented expressions for non-thermal recombination (NTR) in the collisionally thin- and thick-target regimes, claiming that the process could account for a substantial part of the hard X-ray continuum in solar flares usually attributed entirely to thermal and non-thermal bremsstrahlung (NTB). However, we have found the thick-target expression to become unphysical for low cut-offs in the injected electron energy spectrum. We trace this to an error in the derivation, derive a corrected version that is real-valued and continuous for all photon energies and cut-offs, and show that, for thick targets, Brown et al. overestimated NTR emission at small photon energies. The regime of small cut-offs and large spectral indices involve large (reducing) correction factors but in some other thick-target parameter regimes NTR/NTB can still be of the order of unity. We comment on the importance of these results to flare and microflare modeling and spectral fitting. An empirical fit to our results shows that the peak NTR contribution comprises over half of the hard X-ray signal if δ≳6(((E{sub 0c})/(4keV))){sup 0.4}.
Building a useful quantum computer is a grand science and engineering challenge, currently pursued intensely by teams around the world. In the 1980s, Richard Feynman and Yuri Manin observed independently that computers based on quantum mechanics might enable better simulations of quantum phenomena. Their vision remained an intellectual curiosity until Peter Shor published his famous quantum algorithm for integer factoring, and shortly thereafter a proof that errors in quantum computations can be corrected. Since then, quantum computing R&D has progressed rapidly, from small-scale experiments in university physics laboratories to well-funded industrial efforts and prototypes. Hype notwithstanding, quantum computers have yet to solve scientifically or practically important problems -- a target often called quantum utility. In this article, we describe the capabilities of contemporary quantum computers, compare them to the requirements of quantum utility, and illustrate how to track progress from today to utility. We highlight key science and engineering challenges on the road to quantum utility, touching on relevant aspects of our own research.