Search NASA⌕ Search

SEARCH · Search NASA

Results for “sequence design”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 757 records · Page 42
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

ObstacleSense: Low-Power Neuromorphic Vision for Corridor Obstacle Awareness in Low-Level ADAS

The automotive industry’s pursuit of Level 5 autonomy is constrained by substantial perception-compute power requirements, often reaching 1, 000 + watts in full autonomy stacks. Reducing this energy burden requires rethinking perception not only at the high-end autonomy level, but also at the foundational Advanced Driver Assistance Systems (ADAS) level where low-power, safety-critical sensing can have broad impact. Neuromorphic vision provides a promising starting point: HD Dynamic Vision Sensors (DVS) can operate below 100 mW at the sensor level by reporting only asynchronous brightness changes. However, low-power sensing alone is insufficient if downstream perception reintroduces dense, energy-intensive computation. In particular, many event-driven object-detection pipelines still rely on CNN backbones, while purely spiking alternatives often trade away accuracy or ignore deployment constraints. We introduce ObstacleSense, a highly compact, CNN-free hybrid ANN–SNN framework for Level 0–1 forward-corridor obstacle awareness. Instead of performing full-scene object detection with a convolutional feature backbone, ObstacleSense targets the safety-critical question of whether the ego corridor is occupied and how far the nearest obstacle is. The architecture combines polarity-conditioned event encoding, lightweight temporal spiking dynamics, axial spatial mixing, and coarse-to-fine range estimation within a regular fixed-grid compute pattern. This design avoids the dense CNN backbone commonly used in event-based detection while maintaining a small state footprint suitable for eventual small-FPGA deployment. Before hardware mapping, we evaluate the software implementation using a model-side power proxy derived from MACs, weight and activation traffic, and spiking state updates under shared FP16 assumptions. On simulated CARLA event corpora, the deployment-oriented model achieves 0.9464 objectness F1, 0.9978 grid-level mAP, and 0.8987 m distance Mean Absolute Error at an estimated 1.92 mW proxy cost, while maintaining performance on unseen generalization test sequences.

Johnson-Scott, Zac [ORNL]↗
DOE OSTI2025

TransPlatformer

We propose TransPlatformer for translating toxicogenomics from one platform to another. Transcriptomic profiling has evolved through multiple generations of technology, from microarrays (e.g., Affymetrix, CodeLink) to more recent high-throughput sequencing and targeted panels such as S1500+. Microarrays, which dominated gene expression studies in the early 2000s, provided affordable and high-throughput transcript quantification but suffered from cross-hybridization issues and limited dynamic range . RNA-Seq, introduced in the late 2000s, revolutionized transcriptomics by enabling unbiased and comprehensive gene expression analysis, albeit at higher costs and computational demands . Despite advances, many studies rely on historical microarray data, necessitating the translation of legacy data into modern platforms to ensure continuity and comparability. This translation is complicated by factors such as platform-specific probe design, differences in transcript coverage, and batch effects . Existing methods for cross-platform mapping include statistical normalization, machine learning models, and biological anchoring approaches. The ability to translate transcriptomic data between platforms has broad implications, including enhanced meta-analyses, improved toxicological modeling, and better integration of historical datasets with contemporary research. TransPlatformer seeks to contribute to this effort by evaluating translation methodologies and proposing novel strategies to improve cross-platform gene expression harmonization. In this repository there are code examples for TransPlatformer implementation

Cong, Guojing↗
DOE OSTI2024

Targeted Biomining and Machine Learning Approaches in Critical Minerals Revealed by a Biogeochemical Survey of a Coal Mine Drainage Remediation System

Abandoned coal mine drainage (AMD) remediation systems in Pennsylvania can concentrate critical minerals and materials (CMM) at levels comparable to mining-grade ores. Remediation systems have varying engineering features and are open to the environment, resulting in diverse microbial colonization and seasonal climate influences that may impact CMM speciation. The location of CMMs, the types of bacterial communities tolerant of these pollutant conditions, and the influence of localized climate on CMM rich remediation systems are not well characterized. Through a one-year spatiotemporal survey of biogeochemistry at a remediation system, we have initiated the process to address these questions. Rare Earth Elements (REE) ranged 180-1,200 ppm and greater than 1,500 bacterial ASVs were classified via 16S sequencing. Analyses indicate biogeochemical differences are heavily influenced by engineering features. Additionally, REE precipitants correlate strongly with the elements Al, Cu, Zn, Be, and U. Unearthing these trends has refined our line of inquiry to explore biological mining opportunities more closely with these metals. Furthermore, we created a Machine Learning Model for predicting AMD REE content, with 89% accuracy, using the data from this study and several others. Further training data is required to create a more reputable model. Recently, global research efforts have prioritized modeling work or the use of the few historical surveys to design experiments. Through our data, we challenge this approach, emphasizing the importance of expanding fundamental survey efforts prior to advanced product design and experimentation.

critical minerals↗
DOE OSTI2025

Mesh-based super-resolution of fluid flows with multiscale graph neural networks

A graph neural network (GNN) approach is introduced in this work which enables mesh-based three-dimensional super-resolution of fluid flows. In this framework, the GNN is designed to operate not on the full mesh-based field at once, but on localized meshes of elements (or cells) directly. To facilitate mesh-based GNN representations in a manner similar to spectral (or finite) element discretizations, a baseline GNN layer (termed a message passing layer, which updates local node properties) is modified to account for synchronization of coincident graph nodes, rendering compatibility with commonly used element-based mesh connectivities. Furthermore, the architecture is multiscale in nature, and is comprised of a combination of coarse-scale and fine-scale message passing layer sequences (termed processors) separated by a graph unpooling layer. The coarse-scale processor embeds a query element (alongside a set number of neighboring coarse elements) into a single latent graph representation using coarse-scale synchronized message passing over the element neighborhood, and the fine-scale processor leverages additional message passing operations on this latent graph to correct for interpolation errors. Demonstration studies are performed using hexahedral mesh-based data from Taylor–Green Vortex and backward-facing step flow simulations at Reynolds numbers of 1600 and 3200. Through analysis of both global and local errors, the results ultimately show how the GNN is able to produce accurate super-resolved fields compared to targets in both coarse-scale and multiscale model configurations. Reconstruction errors for fixed architectures were found to increase in proportion to the Reynolds number. Geometry extrapolation studies on a separate cavity flow configuration show promising cross-mesh capabilities of the super-resolution strategy.

Backward-facing step↗
DOE OSTI2025

Crystal structure and catalytic mechanism of drimenol synthase, an unusual bifunctional terpene cyclase–phosphatase

Drimenol synthase fromAquimarina spongiae(AsDMS) is a highly unusual chimera that integrates two distinct, sequential isoprenoid processing activities within a single polypeptide chain. AsDMS catalyzes the class II cyclization of farnesyl diphosphate (FPP) to form drimenyl diphosphate, which then undergoes enzyme-catalyzed hydrolysis to yield drimenol, a bioactive sesquiterpene alcohol with antifungal and anticancer properties. Here, we report the X-ray crystal structures of AsDMS and its complex with a sesquiterpene thiol. The AsDMS structure exhibits a didomain architecture consisting of a terpene cyclase β domain and a haloacid dehalogenase-like phosphatase domain, with two distinct active sites located on opposite sides of the protein. Mechanistic studies show that dephosphorylation of the drimenyl diphosphate intermediate proceeds through stepwise hydrolysis such that two equivalents of inorganic phosphate rather than inorganic pyrophosphate are coproducts of the reaction sequence. When the AsDMS reaction is performed in H 2 18 O, 18 O is not incorporated into drimenol, indicating that the hydroxyl oxygen of drimenol originates from the prenyl oxygen of FPP rather than a water molecule from bulk solution. These results correct a mechanistic proposal previously advanced by another group. Surprisingly, AsDMS exhibits substrate promiscuity, catalyzing the conversion of the slowly reactive substrate mimic farnesyl-S-thiolodiphosphate into cyclic and linear sesquiterpene products. Structural and mechanistic insights gained from AsDMS illustrate the functional diversity of terpene biosynthetic enzymes and provide a foundation for engineering “designer cyclase” assemblies capable of generating a wide variety of terpenoid products.

Science & Technology - Other Topics↗
DOE OSTI2023

Hybrid interatomic potential for Sn

To design materials for extreme applications, it is important to understand and predict phase transitions and their influence on material properties under high pressures and temperatures. Atomistic modeling can be a useful tool to assess these behaviors. However, this can be difficult due to the lack of fidelity of the interatomic potentials in reproducing this high pressure and temperature extreme behavior. Here, in this work, a hybrid EAM-R—which is the combination of embedded atom method (EAM) and rapid artificial neural network potential—for Tin (Sn) is described which is capable of accurately modeling the complex sequence of phase transitions between different metallic polymorphs as a function of pressure. This hybrid approach ensures that a basic empirical potential like EAM is used as a lower energy bound. By using the final activation function, the neural network contribution to energy must be positive, assuring stability over the whole configuration space. This implementation has the capacity to reproduce density functional theory results at 6 orders of magnitude slower than a pair potential for molecular dynamics simulation, including elastic and plastic characteristics and relative energies of each phase. Using calculations of the Gibbs free energy, it is demonstrated that the potential precisely predicts the experimentally observed phase changes at temperatures and pressures across the whole phase diagram. At 10.2 GPa, the present potential predicts a first-order phase transition between body-centered tetragonal (BCT) β-Sn and another polymorph of BCT-Sn. This structure transforms into body-centered cubic near the experimentally reported value at 33 GPa. Thus, the Sn potential developed in this paper can be used to study complex deformation mechanisms under extreme conditions of high pressure and strain rates unlike existing potentials. Moreover, the framework developed in this paper can be extended for different material systems with complex phase diagrams.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Advancing Multi-Hazard Risk and Safety Considerations for Aging Nuclear Facilities

While probabilistic risk assessment (PRA) of nuclear facilities is expected to include internal and external hazards for a risk-informed and performance-based design, the current state of practice treats each hazard independently. However, such an independent treatment of hazards may not account for the correlations between different hazards and their response of and damage to the structures, systems, and components (SSCs) in a plant resulting in underestimating the overall risk. This project proposes to advance the multi-hazard PRA of nuclear facilities to more adequately evaluate concurrent hazards and contribute to an increased safety of nuclear plants. A framework for multi-hazard PRA will be developed by identifying concurrent hazard events (both internal and external) and event sequences that include interdependencies through the response of SSCs. An example application of the multi-hazard PRA framework will be demonstrated by considering a generic pressurized water reactor (PWR) subjected to seismic and internal flooding hazards. Computational models for the response of components will be developed to generated multi-hazard fragility surfaces under seismic and flooding loads. A PRA model consisting of event and fault trees will also be developed to quantify the multi-hazard risk profile and compare it with the independent hazard risk profile. Overall, by advancing the multi-hazard PRA of nuclear facilities, this project enhances nuclear safety and reduces costs by mitigating unforeseen consequences caused by correlations between concurrent hazards.

97 - MATHEMATICS AND COMPUTING↗
DOE OSTI2024

High throughput automated characterization of enamel microstructure using synchrotron tomography and optical flow imaging

The remarkable damage-tolerance of enamel has been attributed to its hierarchical microstructure and the organized bands of decussated rods. A thorough characterization of the microscale rod evolution within the enamel is needed to elucidate this complex structure. While prior efforts in this area have made use of single particle tracking to track a single rod evolution to various degrees of success, such a process can be both computationally and labor intensive, limited to the evolution path of a single rod, and is therefore prone to error from potentially tracking outliers. Particle image velocimetry (PIV) is a well-established algorithm to derive field information from image sequences for processes that are time-dependent, such as fluid flows and structural deformation. In this work, we demonstrate the use of PIV in extracting the full-field microstructural distribution of rods within the enamel. Further, enamel samples from a wild African lion were analyzed using high-energy synchrotron X-ray micro-tomography. Results from the PIV analysis provide sufficient full-field information to reconstruct the growth of individual rods that can potentially enable rapid analysis of complex microstructures from high resolution synchrotron datasets. Such information can serve as a template for designing damage-tolerant bioinspired structures for advanced manufacturing.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Implementing two-qubit gates at the quantum speed limit

The speed of elementary quantum gates, particularly two-qubit gates, ultimately sets the limit on the speed at which quantum circuits can operate. In this work, we experimentally demonstrate commonly used two-qubit gates at nearly the fastest possible speed allowed by the physical interaction strength between two superconducting transmon qubits. We achieve this quantum speed limit by implementing experimental gates designed using a machine-learning-inspired optimal control method. Importantly, our method only requires the single-qubit drive strength to be moderately larger than the interaction strength to achieve an arbitrary two-qubit gate close to its analytical speed limit with high fidelity. Thus the method is applicable to a variety of platforms, including those with comparable single-qubit and two-qubit gate speeds, or those with always-on interactions. We expect our method to offer significant speedups for non-native two-qubit gates that are typically achieved with a long sequence of single-qubit and native two-qubit gates.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2024

Reverse Polarizability of Rare Earth Ions (La 3+ , Gd 3+ , Lu 3+ , Y 3+ ) in Tellurite Glasses and Glass Ceramics for Optical Limiting

All-optical modulation using inherent third-order optical nonlinearity of a medium has garnered considerable interest in photonics and optoelectronics. Herein, nonlinear optical (NLO) properties of tellurite glasses and glass ceramics (GCs) containing four different rare earths (RE = La, Gd, Lu, and Y) have been deliberated in near-infrared regions under an ultrafast regime. The La-based glass exhibits ~10 times higher nonlinear refraction (n 2 ) and absorption (α 2 ) than reported NLO materials. The NLO susceptibility [χ (3) ] trend in the studied glasses is La > Gd > Lu > Y, matching with RE 3+ polarizability. Furthermore, Ln 2 Te 6 O 15 nanocrystallite-embedded transparent GCs exhibit a larger NLO coefficient due to the enhanced local field from oxygen vacancies in crystallites. Interestingly, the trend of χ (3) in GCs follows the sequence of Y > Lu > Gd > La, precisely opposite to the glasses. This observation challenges the general polarizability approach of RE 3+ ions, emphasizing that quadratic hyperpolarizability of RE 3+ is pivotal for NLO properties of GCs. Among the studied matrices, Y-containing GCs showed the lowest optical limiting (OL) threshold (5.4 mJ/cm 2 at 800 nm), much lower than those of the reported NLO materials, suggesting its potential as a femtosecond NIR-laser safety material. A combination of large α 2 and n 2 from the studied matrices indicates their advantage for harmonic generation, potentially aiding in the design of ultrafast signal processing devices.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Reductive chemistry of pyrrolic macrocycles: A PCET dichotomy between metal and ligand

Proton-coupled electron transfer (PCET) is central to the reactivity of porphyrins. The coupling of the electron to the proton is central to a porphyrin’s ability to catalyze energy conversion reactions of which the hydrogen evolution reaction (HER) is exemplary. To understand the mechanistic details of the PCET chemistry of porphyrins and related macrocyclic congeners, we have designed hangman constructs that allow a proton, placed in the secondary coordination sphere (off of the hangman backbone), to be coupled to redox transformations at the macrocycle. For metals whose reduction potentials are positive of the porphyrin macrocycle, such as Co and Fe, HER catalysis is confined to PCET transformations of the metal center where the active catalyst for HER is a reduced metal hydride. Alternatively, the reduction potentials of Ni, Zn, and 2H (freebase) porphyrins allow for redox non-innocence of the macrocycle; here the active “hydridic” catalyst is a phlorin, which gives rise to elaborate HER reaction sequences. Beyond HER catalysis, redox non-innocence of Ni, Zn, and 2H porphyrins and related compounds has been informative for providing detailed mechanistic insight into the multi-site PCET hydrogenation of olefinic bonds of the macrocycle. This mini-review unravels the PCET dichotomy between the metal and macrocycle in promoting HER catalysis and novel chemical transformations that give rise to unusual macrocyclic structures.

Chemistry↗
DOE OSTI2024

A Conceptual Design and Economic Assessment of a Chitin Biorefinery Based on Shrimp Processing Wastes

Due to the extensive production of shrimp in captivity, waste generation has increased significantly and has become an environmental problem. The recovery of biomolecules can be an important way to mitigate the environmental problems associated with processing in this sector. In this sense, the present work aimed to evaluate a biorefinery approach for valuing shrimp farming waste to obtain astaxanthin, chitin and chitosan. Stoichiometric segmentation was used as a tool to identify the process steps, whose information on process variables (fresh water consumption, flow rate and reaction conditions) was adapted from the literature. A biorefinery coupled to a shrimp processing plant was proposed for the immediate use of highly perishable biomass, to guarantee the quality of the extracted products and reduce storage and transportation costs. In practice, the biomass treatment sequence adopted (demineralization followed by deproteinization) eliminates the depigmentation step, as the chitin obtained has a lighter tone. The proposed route was evaluated for four scenarios based on two indicators: gross economic potential (EGP) and metrics for inspection of sales and reagents (MISR). The results indicate that economic viability is achieved only for the production of chitosan, resulting in a gross revenue of US$832.50/cycle and a MISR value > 1. The sale of astaxanthin promotes an increase of US$3.73/cycle in the EGP, considered too low for the inclusion of another stage in the process.

astaxanthin↗
DOE OSTI2023

Dual-Responsive Turn-On T 1 Imaging-Guided Mild Photothermia for Precise Apoptotic Cancer Therapy

Apoptosis has gained increasing attention in cancer therapy as an intrinsic signaling pathway, which leads to minimal leakage of waste products from a dying cell to neighboring normal cells. Among various stimuli to trigger apoptosis, mild hyperthermia is attractive but confronts limitations of non-specific heating and acquired resistance from elevated expression of heat shock proteins. Here, a dual-stimulation activated turn-on T 1 imaging-based nanoparticulate system (DAS) is developed for mild photothermia (≈43 °C)-mediated precise apoptotic cancer therapy. In the DAS, a superparamagnetic quencher (ferroferric oxide nanoparticles, Fe 3 O 4 NPs) and a paramagnetic enhancer (Gd-DOTA complexes) are connected via the N6-methyladenine (m 6 A)-caged, Zn 2+ -dependent DNAzyme molecular device. The substrate strand of the DNAzyme contains one segment of Gd-DOTA complex-labeled sequence and another one of HSP70 antisense oligonucleotide. When the DAS is taken up by cancer cells, overexpressed fat mass and obesity-associated protein (FTO) specifically demethylates the m 6</-sup>A group, thereby activating DNAzymes to cleave the substrate strand and simultaneously releasing Gd-DOTA complex-labeled oligonucleotides. The restored T 1 signal from the liberated Gd-DOTA complexes lights up the tumor to guide the location and time of deploying 808 nm laser irradiation. Afterward, locally generated mild photothermia works in concert with HSP70 antisense oligonucleotides to promote apoptosis of tumor cells. This highly integrated design provides an alternative strategy for mild hyperthermia-mediated precise apoptotic cancer therapy.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2022

G2Aero (Separable shape tensors for aerodynamic design) [SWR-22-44]

G2Aero is a flexible and practical tool for design and deformation of 2D airfoils and 3D blades using data-driven approaches. G2Aero utilizes the geometry of matrix manifolds—specifically the Grassmannian—to build a novel framework for representing physics-based separable deformations of shapes. G2Aero offers the flexibility to generate perturbations in a customizable way over any portion of the blade. The G2Aero framework utilizes data-driven methods based on a curated database of physically relevant airfoils. Specific tools include: - principal geodesic analysis over normal coordinate neighborhoods of matrix manifolds; - a variety of data-regularized deformations to nominal 2D airfoil shapes; - Riemannian interpolation connecting a sequence of airfoil cross-sections to build 3D blades from 2D data; - consistent perturbations over the span of interpolated 3D blades based on dominant modes from the data-driven analysis.

Doronina, Olga↗
DOE OSTI2026

CAHS: Context-Aware Homology Search

Protein homology search is foundational to bioinformatics: it supports annotation transfer, structure/function inference, and evolutionary analysis over rapidly expanding sequence repositories (e.g., UniProtKB). Profile hidden Markov models (pHMMs), as implemented in HMMER, remain the most widely trusted approach because they provide statistically calibrated E-values; however, their gap behavior is fixed once a profile is trained, despite biological evidence that insertion/deletion tolerance varies across flexible loops and intrinsically disordered regions. We present CAHS (Context-Aware Homology Search), a lightweight query-time adapter for pHMM search that incorporates learned and biologically motivated signals without changing HMMER's downstream search pipeline or its calibrated E-value reporting. Given a query sequence, CAHS computes per-residue representations from a protein language model and a disorder predictor, maps these to profile coordinates, and modulates only match-state transition rows (gap-open and gap-extension probabilities) while preserving Plan7 constraints. We comprehensively evaluate CAHS across six structurally diverse protein families and multi-domain architectures against a 570k-sequence target corpus. CAHS expands detection capability, retrieving thousands of additional remote homologs at relaxed thresholds by maintaining alignment quality through flexible regions. For multi-domain proteins, context-aware modulation resolves 94% of fragmented alignments. Crucially, CAHS preserves hit-set invariance at stringent operating points (E<10-10), demonstrating increased statistical confidence without inflating false positives. Furthermore, sharper statistical distinction between homologs and background noise during early filter stages yields up to a 3.87× acceleration in end-to-end wall-clock time on high-performance computing clusters. Overall, CAHS illustrates a practical AI-for-science design pattern: augmenting a trusted probabilistic model with query-specific learned signals to improve interpretable, reproducible inference in data-rich biology.

Bhattaram, Swethasree [Georgia Institute of Techno↗
DOE OSTI2021

The F-box protein gene exo-1 is a target for reverse engineering enzyme hypersecretion in filamentous fungi

Carbohydrate active enzymes (CAZymes) are vital for the lignocellulose-based biorefinery. The development of hypersecreting fungal protein production hosts is therefore a major aim for both academia and industry. However, despite advances in our understanding of their regulation, the number of promising candidate genes for targeted strain engineering remains limited. Here, we resequenced the genome of the classical hypersecreting Neurospora crassa mutant exo-1 and identified the causative point of mutation to reside in the F-box protein–encoding gene, NCU09899. The corresponding deletion strain displayed amylase and invertase activities exceeding those of the carbon catabolite derepressed strain ?cre-1, while glucose repression was still mostly functional in ?exo-1. Surprisingly, RNA sequencing revealed that while plant cell wall degradation genes are broadly misexpressed in ?exo-1, only a small fraction of CAZyme genes and sugar transporters are up-regulated, indicating that EXO-1 affects specific regulatory factors. Aiming to elucidate the underlying mechanism of enzyme hypersecretion, we found the high secretion of amylases and invertase in ?exo-1 to be completely dependent on the transcriptional regulator COL-26. Furthermore, misregulation of COL-26, CRE-1, and cellular carbon and nitrogen metabolism was confirmed by proteomics. Finally, we successfully transferred the hypersecretion trait of the exo-1 disruption by reverse engineering into the industrially deployed fungus Myceliophthora thermophila using CRISPR-Cas9. Our identification of an important F-box protein demonstrates the strength of classical mutants combined with next-generation sequencing to uncover unanticipated candidates for engineering. These data contribute to a more complete understanding of CAZyme regulation and will facilitate targeted engineering of hypersecretion in further organisms of interest.

Gabriel, Raphael↗
DOE OSTI2021

Molecular Insight into the β-Sheet Twist and Related Morphology of Self-Assembled Peptide Amphiphile Ribbons

Self-assembly of high-aspect-ratio filaments containing β-sheets has attracted much attention due to potential use in bioengineering and biomedicine. However, precisely predicting the assembled morphologies remains a grand challenge because of insufficient understanding of the self-assembly process. We employed an atomistic model to study the self-assembly of peptide amphiphiles (PAs) containing valine–glutamic acid (VE) dimeric repeats. By changing of the sequence length, the assembly morphology changes from flat ribbon to left-handed twisted ribbon, implying a relationship between β-sheet twist and strength of interstrand hydrogen bonds. The calculations are used to quantify this relationship including both magnitude and sign of the ribbon twist angle. Interestingly, a change in chirality is observed when we introduce the RGD epitope into the C-terminal of VE repeats, suggesting arginine and glycine’s role in suppressing right-handed β-sheet formation. Furthermore, this study provides insight into the relationship between β-sheet twist and self-assembled nanostructures including a possible design rule for PA self-assembly.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗