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At least 55 records · Page 3

Self-Sorting vs Coassembly in Peptide Amphiphile Supramolecular Nanostructures

The functionality of supramolecular nanostructures can be expanded if systems containing multiple components are designed to either self-sort or mix into coassemblies. This is critical to gain the ability to craft self-assembling materials that integrate functions, and our understanding of this process is in its early stages. Here, in this work, we have utilized three different peptide amphiphiles with the capacity to form β-sheets within supramolecular nanostructures and found binary systems that self-sort and others that form coassemblies. This was measured using atomic force microscopy to reveal the nanoscale morphology of assemblies and confocal laser scanning microscopy to determine the distribution of fluorescently labeled monomers. We discovered that PA assemblies with opposite supramolecular chirality self-sorted into chemically distinct nanostructures. In contrast, the PA molecules that formed a mixture of right-handed, left-handed, and flat nanostructures on their own were able to coassemble with the other PA molecules. We attribute this phenomenon to the energy barrier associated with changing the handedness of a β-sheet twist in a coassembly of two different PA molecules. This observation could be useful for designing biomolecular nanostructures with dual bioactivity or interpenetrating networks of PA supramolecular assemblies.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Liver 4DMRI: A retrospective image-based sorting method

Purpose: Four-dimensional magnetic resonance imaging (4DMRI) is an emerging technique in radiotherapy treatment planning for organ motion quantification. In this paper, the authors present a novel 4DMRI retrospective image-based sorting method, providing reduced motion artifacts than using a standard monodimensional external respiratory surrogate. Methods: Serial interleaved 2D multislice MRI data were acquired from 24 liver cases (6 volunteers + 18 patients) to test the proposed 4DMRI sorting. Image similarity based on mutual information was applied to automatically identify a stable reference phase and sort the image sequence retrospectively, without the use of additional image or surrogate data to describe breathing motion. Results: The image-based 4DMRI provided a smoother liver profile than that obtained from standard resorting based on an external surrogate. Reduced motion artifacts were observed in image-based 4DMRI datasets with a fitting error of the liver profile measuring 1.2 ± 0.9 mm (median ± interquartile range) vs 2.1 ± 1.7 mm of the standard method. Conclusions: The authors present a novel methodology to derive a patient-specific 4DMRI model to describe organ motion due to breathing, with improved image quality in 4D reconstruction.

60 APPLIED LIFE SCIENCES↗

Raman-based sorting of microbial cells to link functions to their genes

In our recent work, we developed an optofluidic platform that allows a direct link to be made between the phenotypes (functions) and the genotypes (genes) of microbial cells within natural communities. By combining stable isotope probing, optical tweezers, Raman microspectroscopy, and microfluidics, the platform performs automated Raman-based sorting of taxa from within a complex community in terms of their functional properties. In comparison with manual sorting approaches, our method provides high throughput (up to 500 cells per hour) and very high sorting accuracy (98.3 ± 1.7%), and significantly reduces the human labour required. The system provides an efficient manner to untangle the contributions of individual members within environmental and host-associated microbiomes. In this News and Thoughts, we provide an overview of our platform, describe potential applications, suggest ways in which the system could be improved, and discuss future directions in which Raman-based analysis of microbial populations might be developed.

59 BASIC BIOLOGICAL SCIENCES↗

Parallel sorting algorithm classification: is manual instrumentation necessary?

Understanding parallel algorithms is crucial for accelerating scientific simulations on complex, distributed memory, high-performance computers. Modern algorithm classification approaches learn semantics directly from source code to differentiate between algorithms, however, accessing source code is not always possible. We can learn about parallel algorithms from observing their performance, as programs running the same algorithms and using the same hardware should exhibit similar performance characteristics. We present an approach to learn algorithm classes from parallel performance data directly in order to classify algorithms without access to the source code. We extend previous work to enable classifying parallel sorting algorithms using automatic instrumentation instead of requiring manual region annotations in the source code. In this work, we design and demonstrate a study for classification of parallel sorting algorithms using parallel performance data collected from automatic instrumentation, and evaluate the performance of our new methodology on classification. We leverage Caliper to collect the performance data, Thicket for our exploratory data analysis (EDA), and PyTorch and Scikit-learn to evaluate the effectiveness of random forests, support vector machines (SVMs), decision trees, neural networks, and logistic regressions on parallel performance data. Additionally, we study noise in parallel performance data, whether the removal of noise and pre-processing of the data is necessary to accurately classify parallel sorting algorithms, and determine the effectiveness of features created from performance data. In conclusion, we demonstrate classification accuracy for these five different models of up to 97.7% across four different parallel algorithm classes.

Algorithm Classification↗

A Novel Fast Helical 4D-CT Acquisition Technique to Generate Low-Noise Sorting Artifact–Free Images at User-Selected Breathing Phases

Purpose: To develop a novel 4-dimensional computed tomography (4D-CT) technique that exploits standard fast helical acquisition, a simultaneous breathing surrogate measurement, deformable image registration, and a breathing motion model to remove sorting artifacts. Methods and Materials: Ten patients were imaged under free-breathing conditions 25 successive times in alternating directions with a 64-slice CT scanner using a low-dose fast helical protocol. An abdominal bellows was used as a breathing surrogate. Deformable registration was used to register the first image (defined as the reference image) to the subsequent 24 segmented images. Voxel-specific motion model parameters were determined using a breathing motion model. The tissue locations predicted by the motion model in the 25 images were compared against the deformably registered tissue locations, allowing a model prediction error to be evaluated. A low-noise image was created by averaging the 25 images deformed to the first image geometry, reducing statistical image noise by a factor of 5. The motion model was used to deform the low-noise reference image to any user-selected breathing phase. A voxel-specific correction was applied to correct the Hounsfield units for lung parenchyma density as a function of lung air filling. Results: Images produced using the model at user-selected breathing phases did not suffer from sorting artifacts common to conventional 4D-CT protocols. The mean prediction error across all patients between the breathing motion model predictions and the measured lung tissue positions was determined to be 1.19 ± 0.37 mm. Conclusions: The proposed technique can be used as a clinical 4D-CT technique. It is robust in the presence of irregular breathing and allows the entire imaging dose to contribute to the resulting image quality, providing sorting artifact–free images at a patient dose similar to or less than current 4D-CT techniques.

62 RADIOLOGY AND NUCLEAR MEDICINE↗

Contorted Octabenzocircumbiphenyl Sorts Semiconducting Single-Walled Carbon Nanotubes with Structural Specificity

Here, we use a nonplanar aromatic molecule, contorted octabenzocircumbiphenyl (c-OBCB), to sort semiconducting single-walled carbon nanotubes (SWNTs) by their chiral angles. From absorption spectroscopy, photoluminescence excitation spectroscopy, and Raman spectroscopy studies, we find that c-OBCB preferentially binds and sorts for a number of semiconducting carbon nanotubes with chiral angles greater than 12°. Molecular dynamics simulations reveal that the contorted aromatic core of c-OBCB binds strongly to only certain SWNTs, especially those with matching curvature, and that this discriminatory binding interaction is reinforced by preferences of the side chains on the c-OBCB to stick to SWNT surface rather than interact with the solvent. This opens the door to side chain/solvent engineering to bias the selection of certain (m,n) SWNT variants. We also investigate the temperature dependence of hole mobility in field-effect transistors comprising c-OBCB-sorted semiconducting carbon-nanotube networks and find hole transport in these networks to be thermally activated.

36 MATERIALS SCIENCE↗

Layer-by-Layer Sorting of Rhenium Disulfide via High-Density Isopycnic Density Gradient Ultracentrifugation

Isopycnic density gradient ultracentrifugation (iDGU) has been widely applied to sort nanomaterials by their physical and electronic structure. Yet, the commonly used density-gradient medium iodixanol has a finite maximum buoyant density that prevents the use of iDGU for high-density nanomaterials. Here, we overcome this limit by adding cesium chloride (CsCl) to iodixanol, thus increasing its maximum buoyant density to the point where the high-density two-dimensional nanomaterial rhenium disulfide (ReS 2 ) can be sorted in a layer-by-layer manner with iDGU. The resulting aqueous ReS 2 dispersions show photoluminescence at ~1.5 eV, which is consistent with its direct bandgap semiconductor electronic structure. Moreover, photocurrent measurements on thin films formed from solution-processed ReS 2 show a spectral response that is consistent with optical absorbance and photoluminescence data. In addition to offering a pathway for effective solution processing of ReS 2 , this work establishes a general methodology for sorting high-density nanomaterials via iDGU.

36 MATERIALS SCIENCE↗

Critical role of the sorting polymer in carbon nanotube-based minority carrier devices

A prerequisite for carbon nanotube-based optoelectronic devices is the ability to sort them into a pure semiconductor phase. One of the most common sorting routes is enabled through using specific wrapping polymers. Here we show that subtle changes in the polymer structure can have a dramatic influence on the figures of merit of a carbon nanotube-based photovoltaic device. By comparing two commonly used polyfluorenes (PFO and PFO-BPy) for wrapping (7,5) and (6,5) chirality SWCNTs, we demonstrate that they have contrasting effects on the device efficiency. We attribute this to the differences in their ability to efficiently transfer charge. Although PFO may act as an efficient interfacial layer at the anode, PFO-BPy, having the additional pyridine side groups, forms a high resistance layer degrading the device efficiency. By comparing PFO|C 60 and C 60 -only devices, we found that presence of a PFO layer at low optical densities resulted in the increase of all three solar cell parameters, giving nearly an order of magnitude higher efficiency over that of C 60 -only devices. In addition, with a relatively higher contribution to photocurrent from the PFO-C 60 interface, an open circuit voltage of 0.55 V was obtained for PFO-(7,5)-C 60 devices. On the other hand, PFO-BPy does not affect the open circuit voltage but drastically reduces the short circuit current density. Lastly, these results indicate that the charge transport properties and energy levels of the sorting polymers have to be taken into account to fully understand their effect on carbon nanotube-based solar cells.

14 SOLAR ENERGY↗

A common assembly module in injectisome and flagellar type III secretion sorting platforms

Translocating proteins across the double membrane of Gram-negative bacteria, type III secretion systems (T3SS) occur in two evolutionarily related forms: injectisomes, delivering virulence factors into host cells, and the flagellar system, secreting the polymeric filament used for motility. While both systems share related elements of a cytoplasmic sorting platform that facilitates the hierarchical secretion of protein substrates, its assembly and regulation remain unclear. Here we describe a module mediating the assembly of the sorting platform in both secretion systems, and elucidate the structural basis for segregation of homologous components among these divergent T3SS subtypes sharing a common cytoplasmic milieu. These results provide a foundation for the subtype-specific assembly of T3SS sorting platforms and will support further mechanistic analysis and anti-virulence drug design.

Science & Technology - Other Topics↗

Anti-Hermitian photodetector facilitating efficient subwavelength photon sorting

The ability to split an incident light beam into separate wavelength bands is central to a diverse set of optical applications, including imaging, biosensing, communication, photocatalysis, and photovoltaics. Entirely new opportunities are currently emerging with the recently demonstrated possibility to spectrally split light at a subwavelength scale with optical antennas. Unfortunately, such small structures offer limited spectral control and are hard to exploit in optoelectronic devices. Here, we overcome both challenges and demonstrate how within a single-layer metafilm one can laterally sort photons of different wavelengths below the free-space diffraction limit and extract a useful photocurrent. This chipscale demonstration of anti-Hermitian coupling between resonant photodetector elements also facilitates near-unity photon-sorting efficiencies, near-unity absorption, and a narrow spectral response (~30 nm) for the different wavelength channels. This work opens up entirely new design paradigms for image sensors and energy harvesting systems in which the active elements both sort and detect photons.

36 MATERIALS SCIENCE↗

Microspherical photonics: Sorting resonant photonic atoms by using light

A method of sorting microspheres by resonant light forces in vacuum, air, or liquid is proposed. Based on a two-dimensional model, it is shown that the sorting can be realized by allowing spherical particles to traverse a focused beam. Under resonance with the whispering gallery modes, the particles acquire significant velocity along the beam direction. This opens a unique way of large-volume sorting of nearly identical photonic atoms with 1/Q accuracy, where Q is the resonance quality factor. This is an enabling technology for developing super-low-loss coupled-cavity structures and devices.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

Microfluidic sorting of protein nanocrystals by size for X-ray free-electron laser diffraction

The advent and application of the X-ray free-electron laser (XFEL) has uncovered the structures of proteins that could not previously be solved using traditional crystallography. While this new technology is powerful, optimization of the process is still needed to improve data quality and analysis efficiency. One area is sample heterogeneity, where variations in crystal size (among other factors) lead to the requirement of large data sets (and thus 10–100 mg of protein) for determining accurate structure factors. To decrease sample dispersity, we developed a high-throughput microfluidic sorter operating on the principle of dielectrophoresis, whereby polydisperse particles can be transported into various fluid streams for size fractionation. Using this microsorter, we isolated several milliliters of photosystem I nanocrystal fractions ranging from 200 to 600 nm in size as characterized by dynamic light scattering, nanoparticle tracking, and electron microscopy. Sorted nanocrystals were delivered in a liquid jet via the gas dynamic virtual nozzle into the path of the XFEL at the Linac Coherent Light Source. We obtained diffraction to ∼4 Å resolution, indicating that the small crystals were not damaged by the sorting process. We also observed the shape transforms of photosystem I nanocrystals, demonstrating that our device can optimize data collection for the shape transform-based phasing method. Using simulations, we show that narrow crystal size distributions can significantly improve merged data quality in serial crystallography. From this proof-of-concept work, we expect that the automated size-sorting of protein crystals will become an important step for sample production by reducing the amount of protein needed for a high quality final structure and the development of novel phasing methods that exploit inter-Bragg reflection intensities or use variations in beam intensity for radiation damage-induced phasing. This method will also permit an analysis of the dependence of crystal quality on crystal size.

36 MATERIALS SCIENCE↗

Sorting process of nanoparticles and applications of same

A sensing platform includes a substrate, an array of wells formed in the substrate, and sorted nanoparticles filling in the array of wells such that each well contains a single sorted nanoparticle, wherein the sorted nanoparticles are in a cluster state.

77 NANOSCIENCE AND NANOTECHNOLOGY↗

Sorting cast and wrought aluminum

A material sorting system sorts materials utilizing a vision system that implements a machine learning system in order to identify or classify each of the materials, which are then sorted into separate groups based on such an identification or classification determining that the materials are composed of either wrought aluminum or cast aluminum.

36 MATERIALS SCIENCE↗

River Organic Carbon Fluxes Modulated by Hydrodynamic Sorting of Particulate Organic Matter

Rivers regulate the global carbon cycle by transferring particulate organic carbon (POC) from terrestrial landscapes to marine sedimentary basins, but the processes controlling the amount and composition of fluvially exported POC are poorly understood. We propose that hydrodynamic sorting processes modify POC fluxes during fluvial transit. We test this hypothesis by studying POC transported along a ~1,200 km reach of the Rio Bermejo, Argentina. Nanoscale secondary ion mass spectrometry revealed that POC was either fine, mineral-associated organic matter, or coarse discrete organic particles. Mineral-associated POC is more resistant to oxidation and has a lower particle settling velocity than discrete POC. Consequently, hydraulic sorting and downstream fining amplify the proportion of fine, mineral-associated POC from ~55% to ~78% over 1,220 km of downstream transit. This suggests that mineral-associated POC has a greater probability of export and preservation in marine basins than plant detritus, which may be oxidized to CO 2 during transit.

58 GEOSCIENCES↗

Photon sorting in the near field using subwavelength cavity arrays in the near-infrared

A frequency selective metasurface capable of sorting photons in the near-infrared spectral range is designed, fabricated, and characterized. The metasurface, a periodic array of dielectric cylindrical cavities in a gold film, localizes and transmits light of two spectral frequency bands into spatially separated cavities, resulting in near-field light splitting. The design and fabrication methodologies of the metasurface are discussed. The transmittance and photon sorting properties of the designed structure is simulated numerically and the measured transmission is presented.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

Using sorted invariant mass variables to evade combinatorial ambiguities in cascade decays

The classic method for mass determination in a SUSY-like cascade decay chain relies on measurements of the kinematic endpoints in the invariant mass distributions of suitable collections of visible decay products. However, the procedure is complicated by combinatorial ambiguities: e.g., the visible final state particles may be indistinguishable (as in the case of QCD jets), or one may not know the exact order in which they are emitted along the decay chain. In order to avoid such combinatorial ambiguities, we propose to treat the nal state particles fully democratically and consider the sorted set of the invariant masses of all possible partitions of the visible particles in the decay chain. In particular, for a decay to N visible particles, one considers the sorted sets of all possible n-body invariant mass combinations (2≤ n≤ N) and determines the kinematic endpoint m (n,r) max of the distribution of the r-th largest n-body invariant mass m (n,r) for each possible value of n and r. For the classic example of a squark decay in supersymmetry, we provide analytical formulas for the interpretation of these endpoints in terms of the underlying physical masses. We point out that these measurements can be used to determine the structure of the decay topology, e.g., the number and position of intermediate on-shell resonances.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

Delamination-informed lifecycle decisions: A dielectric and machine learning framework for composite sorting and recycling

Composite materials are widely used in aerospace, marine, and automotive sectors due to their high strength-to-weight ratio and durability. However, their long-term reliability can be compromised by damage accumulation. Specifically, delamination initiation serves as a precursor to structural failure, which is often difficult to detect during damage inspection. Identifying and sorting delamination initiation in samples not only increases operational safety while providing critical information for end-of-life decisions, which influences both the service life extension value and the efficiency of fiber extraction during recycling. This research addresses two challenges: (1) developing a nondestructive, ex-situ framework to sort composite materials based on damage severity, particularly delamination, and (2) understanding how damage in composites influences resin removal during pyrolysis. Both experimental work and finite element analysis were performed to predict critical stress levels that are associated with delamination onset. Based on these results, three loading levels 50 %, 75 %, and 90 % of maximum stress, were selected for controlled experiments, generating composite samples with varying extents of damage for machine learning model training. Microscopic imaging of these samples confirmed the damage progression from matrix cracking to delamination, validating the computational predictions. We explored supervised machine learning using dielectric measurements to classify damage states. Preliminary results show an artificial neural network can identify early delamination which is a potential precursor to failure, with 94.44 % accuracy on our dataset. A parallel investigation into the effect of damage severity on pyrolysis recycling showed that heavily delaminated samples required significantly less energy for comparable matrix removal than undamaged samples.

dielectric variables↗