Bourns College of Engineering

We present an open-source software package, VAN-DAMME (Versatile Approaches to Numerically Design, Accelerate, and Manipulate Magnetic Excitations), for massively-parallelized quantum optimal control (QOC) calculations of multi-qubit systems. To enable large QOC calculations, the VAN-DAMME software package utilizes symmetry-based techniques with custom GPU-enhanced algorithms. This combined approach allows for the simultaneous computation of hundreds of matrix exponential propagators that efficiently leverage the intra-GPU parallelism found in high-performance GPUs. In addition, to maximize the computational efficiency of the VAN-DAMME code, we carried out several extensive tests on data layout, computational complexity, memory requirements, and performance. These extensive analyses allowed us to develop computationally efficient approaches for evaluating complex-valued matrix exponential propagators based on Padé approximants. To assess the computational performance of our GPU-accelerated VAN-DAMME code, we carried out QOC calculations of systems containing 10 - 15 qubits, which showed that our GPU implementation is 18.4× faster than the corresponding CPU implementation. Our GPU-accelerated enhancements allow efficient calculations of multi-qubit systems, which can be used for the efficient implementation of QOC applications across multiple domains. Program summary: Program Title: VAN-DAMME CPC Library link to program files:: https://doi.org/10.17632/zcgw2n5bjf.1 Licensing provisions: GNU General Public License 3 Programming language: C++ and CUDA Nature of problem: The VAN-DAMME software package utilizes GPU-accelerated routines and new algorithmic improvements to compute optimized time-dependent magnetic fields that can drive a system from a known initial qubit configuration to a specified target state with a large (≈1) transition probability. Solution method: Quantum control, GPU acceleration, analytic gradients, matrix exponential, and gradient ascent optimization.

Manta rays use wing-like pectoral fins for intriguing oscillatory swimming. It provides rich inspiration for designing potentially fast, efficient, and maneuverable soft swimming robots, which, however, have yet to be realized. It remains a grand challenge to combine fast speed, high efficiency, and high maneuverability in a single soft swimmer while using simple actuation and control. Here, we report leveraging spontaneous snapping stroke in the monostable flapping wing of a manta-like soft swimmer to address the challenge. The monostable wing is pneumatically actuated to instantaneously snap through to stroke down, and upon deflation, it will spontaneously stroke up by snapping back to its initial state, driven by elastic restoring force, without consuming additional energy. This largely simplifies designs, actuation, and control for achieving a record-high speed of 6.8 body length per second, high energy efficiency, and high maneuverability and collision resilience in navigating through underwater unstructured environments with obstacles by simply tuning single-input actuation frequencies.

We present a new software module, QRCODE (Quantum Research for Calculating Optically Driven Excitations), for massively parallelized real-time time-dependent density functional theory (RT-TDDFT) calculations of periodic systems in the open-source Qbox software package. Our approach utilizes a custom implementation of a fast Fourier transformation scheme that significantly reduces inter-node message passing interface (MPI) communication of the major computational kernel and shows impressive scaling up to 16,344 CPU cores. In addition to improving computational performance, QRCODE contains a suite of various time propagators for accurate RT-TDDFT calculations. As benchmark applications of QRCODE, we calculate the current density and optical absorption spectra of hexagonal boron nitride (h-BN) and photo-driven reaction dynamics of the ozone-oxygen reaction. We also calculate the second and higher harmonic generation of monolayer and multi-layer boron nitride structures as examples of large material systems. Our optimized implementation of RT-TDDFT in QRCODE enables large-scale calculations of real-time electron dynamics of chemical and material systems with enhanced computational performance and impressive scaling across several thousand CPU cores.

Single-cell Hi-C (scHi-C) techniques have significantly advanced our understanding of the 3D genome organization, providing crucial insights into the spatial genome architecture within individual nuclei. Numerous computational and statistical methods have been developed to analyze scHi-C data, with embedding methods playing a key role. Embedding reduces the dimensionality of complex scHi-C contact maps, making it easier to extract biologically meaningful patterns. These methods not only enhance cell clustering based on chromatin structures but also facilitate visualization and other downstream analyses. Most scHi-C embedding methods incorporate strategies such as normalization and imputation to address the inherent sparsity of scHi-C data, thereby further improving data quality and interpretability. In this review, we systematically examine the existing methods designed for scHi-C embedding, outlining their methodologies and discussing their capabilities in handling normalization and imputation. Additionally, we present a comprehensive benchmarking analysis to compare both embedding techniques and their clustering performances. This review serves as a practical guide for researchers seeking to select suitable scHi-C embedding tools, ultimately contributing to the understanding of the 3D organization of the genome.

Significant efforts have been devoted to investigating the oxidation of MXenes in various environments. However, the underlying mechanism of MXene oxidation and its dependence on the electrode potential remain poorly understood. Here we show the oxidation behavior of MXenes under the working conditions of electrochemical processes in terms of kinetics and thermodynamics by using constant-potential ab initio simulations. The theoretical results indicate that the potential effects can be attributed to the nucleophilic attack of water molecules on metal atoms, similar to that taking place in the Oxygen Evolution Reaction. Building upon these findings, we deduced the oxidation potential of the common MXenes, and proposed antioxidant strategies for MXene. Finally, we demonstrated that MBenes, the boron analogs of MXenes, may undergo a similar nucleophilic attack in water and inferred that molecule-induced Walden inversion is widely present in material reconstructions. This work contributes to a fundamental understanding MXene stability at the atomic level, and promotes the transition in materials discovery from trial-and-error synthesis to rational design.

The global prevalence of counterfeit and low-quality pharmaceuticals poses significant health risks and challenges in medical treatments, creating a need for rapid and reliable drug screening technologies. This study introduces a cost-effective electrochemical paper-based device (ePAD) modified with functionalized bamboo-derived biochar (BCF) for the detection of paracetamol in substandard medicines. The sensor was fabricated using a custom 3D-printed stencil in PLA, designed for efficient production, and a 60:40 (m/m) graphite (GR) and glass varnish (GV) conductive ink, resulting in a robust and sensitive platform. The electroactive area of the ePAD/BCF sensor was determined as 0.37 cm2. Characterization via SEM and cyclic voltammetry (CV) verified its structural and electrochemical stability. The sensor demonstrated linear detection of paracetamol from 5.0 to 60.0 µmol L-1 with a detection limit of 3.50 µmol L-1. Interference studies showed high selectivity, with recoveries of over 90%, and the sensor successfully quantified paracetamol in commercial analgesic and anti-flu samples. This sustainable, bamboo-based ePAD offers a promising solution for rapid on-site pharmaceutical quality control, with significant potential to enhance drug screening accuracy.

Since its appearance in [Cheng, X.; Zhang, Y.; Jónsson, E.; Jónsson, H.; Weber, P. M. Nat. Commun. 2016, 7, 11013] and recent re-investigation in [Gałyńska, M.; Ásgeirsson, V.; Jónsson, H.; Bjornsson, R. J. Phys. Chem. Lett., 2021, 12, 1250-1255], the dimethylpiperazine cation (DMP+) has generated considerable discussion and controversy in the scientific literature over the existence of stable, local energy minima in this molecular system. Specifically, prior assumptions that the Rydberg state and radical cation of DMP are similar have led to significant confusion and debate regarding the accuracy of various quantum chemistry methods and the existence of stable configurations of DMP+ itself. The purpose of this Viewpoint is to highlight recent studies that call into question the main findings in the previously mentioned works as well as present new CCSDT (Coupled-Cluster with Single, Double, and Triple excitations) calculations to finally bring closure to this controversy.

The accurate and effective determination of antimicrobial resistance is essential to limiting the spread of infectious diseases and ensuring human health. Herein, a simple, accurate, and high-throughput phage-based colorimetric sensing strategy was developed for antimicrobial susceptibility testing (AST). Taking advantage of the CRISPR/Cas9 system, the genome of the T4 phage was modularly engineered to carry lacZ-α (lacZa), a marker gene encoding the α-fragment of β-galactosidase (β-gal). T4lacZa phages were identified by blue-white selection and then used for a biosensing application. In this strategy, the bacterial solution is exposed to the T4lacZa phage, causing target bacteria to overexpress β-gal. Upon the addition of a colorimetric substrate, the β-gal initiates an enzymatic reaction, resulting in a solution color change from yellow to red. This sensing strategy offers a visual way to monitor bacterial growth in the presence of antibiotics, enabling the determination of bacterial antimicrobial susceptibility. As a proof of concept, our developed sensing strategy was successfully applied to identify 9 different multidrug-resistant Escherichia coli (E. coli) in urine samples with 100% specificity. Compared with conventional disk diffusion susceptibility tests, the engineered phage-based sensing strategy can shorten the detection time by at least half without losing detection sensitivity, providing an alternative high-throughput method for AST in clinical diagnosis.

Current delivery practices for insecticide active ingredients are inefficient with only a fraction reaching their intended target. Herein, we developed carbon dot based nanocarriers with molecular baskets (γ-cyclodextrin) that enhance the delivery of active ingredients into insects (southern green stink bugs, Nezara viridula L.) via their tarsal pores. Nezara viridula feeds on leguminous plants worldwide and is a primary pest of soybeans. After two days of exposure, most of the nanocarriers and their active ingredient cargo (>85%) remained on the soybean leaf surface, rendering them available to the insects. The nanocarriers enter stink bugs through their tarsi, enhancing the delivery of a fluorescent chemical cargo by 2.6 times. The insecticide active ingredient nanoformulation (10 ppm) was 25% more effective in controlling the stink bugs than the active ingredient alone. Styletectomy experiments indicated that the improved active ingredient efficacy was due to the nanoformulation entering through the insect tarsal pores, consistent with fluorescent chemical cargo assays. This new nanopesticide approach offers efficient active ingredient delivery and improved integrated pest management for a more sustainable agriculture.