Bourns College of Engineering

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.

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.

Efficient heterogeneous catalyst design primarily focuses on engineering the active sites or supports, often neglecting the impact of trace impurities on catalytic performance. Herein, we demonstrate that even trace amounts of sulfate (SO42-) residuals on Ru/TiO2 can totally change the CO2 reduction from methanation to reverse-water gas shift (RWGS) reaction under atmospheric pressure. We reveal that air annealing causes the trace amount of SO42- to migrate from TiO2 to Ru/TiO2 interface, leading to the significant changes in product selectivity from CH4 to CO. Detailed characterizations and DFT calculations show that the sulfate at Ru/TiO2 interface notably enhances the H transfer from Ru particles to the TiO2 support, weakening the CO intermediate activation on Ru particles and inhibiting the further hydrogenation of CO to CH4. This discovery highlights the vital role of trace impurities in CO2 hydrogenation reaction, and also provides broad implications for the design and development of more efficient and selective heterogeneous catalysts.

Computer-assisted diagnosis (CAD) plays a key role in cancer diagnosis or screening. Whereas, current CAD performs poorly on whole slide image (WSI) analysis, and thus fails to generalize well. This research aims to develop an automatic classification system to distinguish between different types of carcinomas. Obtaining rich deep features in multi-class classification while achieving high accuracy is still a challenging problem. The detection and classification of cancerous cells in WSI are quite challenging due to the misclassification of normal lumps and cancerous cells. This is due to cluttering, occlusion, and irregular cell distribution. Researchers in the past mostly obtained the hand-crafted features while neglecting the above-mentioned challenges which led to a reduction of the classification accuracy. To mitigate this problem we proposed an efficient dual attention-based network (CytoNet). The proposed network is composed of two main modules (i) Efficient-Net and (ii) Dual Attention Module (DAM). Efficient-Net is capable of obtaining higher accuracy and enhancing efficiency as compared to existing Convolutional Neural Networks (CNNs). It is also useful to obtain the most generic features as it has been trained on ImageNet. Whereas DAM is very robust in obtaining attention and targeted features while negating the background. In this way, the combination of an efficient and attention module is useful to obtain the robust, and intrinsic features to obtain comparable performance. Further, we evaluated the proposed network on two well-known datasets (i) Our generated thyroid dataset (ii) Mendeley Cervical dataset (Hussain in Data Brief, 2019) with enhanced performance compared to their counterparts. CytoNet demonstrated a 99% accuracy rate on the thyroid dataset in comparison to its counterpart. The precision, recall, and F1-score values achieved on the Mendeley Cervical dataset are 0.992, 0.985, and 0.977, respectively. The code implementation is available on GitHub. https://github.com/naveedilyas/CytoNet-An-Efficient-Dual-Attention-based-Automatic-Prediction-of-Cancer-Sub-types-in-Cytol.

We recently completed the Fund Consciousness Science! Project: a workshop and subawards program aimed to align United States federal funding mechanisms and consciousness research. Here we describe the project’s motivation, execution, and outcomes to motivate similar efforts both locally and globally.

Biodegradable polymer-based nanocomposite coatings provide multiple advantages to modulate the corrosion resistance and cytocompatibility of magnesium (Mg) alloys for biomedical applications. Biodegradable poly(glycerol sebacate) (PGS) is a promising candidate used for medical implant applications. In this study, we synthesized a new PGS nanocomposite system consisting of hydroxyapatite (HA) and magnesium oxide (MgO) nanoparticles and developed a spray coating process to produce the PGS nanocomposite layer on pretreated Mg substrates, which improved the coating adhesion at the interface and their cytocompatibility with bone marrow derived mesenchymal stem cells (BMSCs). Prior to the spray coating process of polymer-based nanocomposites, the Mg substrates were pretreated in alkaline solutions to enhance the interfacial adhesion strength of the polymer-based nanocomposite coatings. The addition of HA and MgO nanoparticles (nHA and nMgO) to the PGS matrix, as well as the alkaline pretreatment of the Mg substrates, significantly enhanced the interfacial adhesion strength when compared with the PGS coating on the nontreated Mg control. The average BMSC adhesion densities were higher on the PGS/nHA/nMgO coated Mg than the noncoated Mg controls under direct contact conditions. Moreover, the addition of nHA and nMgO to the PGS matrix and coating the nanocomposite onto Mg substrates increased the average BMSC adhesion density when compared with the PGS/nHA/nMgO coated titanium (Ti) and PGS coated Mg controls under direct contact. Therefore, the spray coating process of PGS/nHA/nMgO nanocomposites on Mg substrates or other biodegradable metal substrates could provide a promising surface treatment strategy for biodegradable implant applications.

Despite promising advancements, closed-loop neurostimulation for drug-resistant epilepsy (DRE) still relies on manual tuning and produces variable outcomes, while automated predictable algorithms remain an aspiration. As a fundamental step towards addressing this gap, here we study predictive dynamical models of human intracranial EEG (iEEG) response under parametrically rich neurostimulation. Using data from n = 13 DRE patients, we find that stimulation-triggered switched-linear models with ~300 ms of causal historical dependence best explain evoked iEEG dynamics. These models are highly consistent across different stimulation amplitudes and frequencies, allowing for learning a generalizable model from abundant STIM OFF and limited STIM ON data. Further, evoked iEEG in nearly all subjects exhibited a distance-dependent pattern, whereby stimulation directly impacts the actuation site and nearby regions (≲ 20 mm), affects medium-distance regions (20 ~ 100 mm) through network interactions, and hardly reaches more distal areas (≳ 100 mm). Peak network interaction occurs at 60 ~ 80 mm from the stimulation site. Due to their predictive accuracy and mechanistic interpretability, these models hold significant potential for model-based seizure forecasting and closed-loop neurostimulation design.

Among the fascinating phenomena observed in two-dimensional (2D) magnets, the magneto-exciton effect stands out as a pivotal link between optics and magnetism. Although the excitonic effect has been revealed and exhibits a considerable correlation with the spin structures in certain 2D magnets, the underlying mechanism of the magneto-exciton effect remains underexplored, especially under high magnetic fields. Here we perform a systematic investigation of the spin-exciton coupling in 2D antiferromagnetic NiPS3 under high magnetic fields. When an in-plane magnetic field is applied, the exceptional sharp excitonic emission at ~1.4756 eV exhibits a Zeeman-like splitting with g ≈ 2.0, experimentally identifying the exciton as an excitation of dominant triplet-singlet character. By examining the polarization of excitonic emission and simulating the spin evolution, we further verify the correlation between excitonic emission and Néel vector in NiPS3. Our work elucidates the mechanism behind the spin-exciton coupling in NiPS3 and establishes a strategy for optically probing the spin evolutions in 2D magnets.

Internal combustion engine fueled by carbon-free hydrogen (H2-ICE) offers a promising alternative for sustainable transportation. Herein, we report a facile and universal strategy through the physical mixing of Pt catalyst with zeolites to significantly improve the catalytic performance in the selective catalytic reduction of nitrogen oxides (NOx) with H2 (H2-SCR), a process aiming at NOx removal from H2-ICE. Via the physical mixing of Pt/TiO2 with Y zeolite (Pt/TiO2 + Y), a remarkable enhancement of NOx reduction activity and N2 selectivity was simultaneously achieved. The incorporation of Y zeolite effectively captured the in-situ generated water, fostering a water-rich environment surrounding the Pt active sites. This environment weakened the NO adsorption while concurrently promoting the H2 activation, leading to the strikingly elevated H2-SCR activity and N2 selectivity on Pt/TiO2 + Y catalyst. This study provides a unique, easy and sustainable physical mixing approach to achieve proficient heterogeneous catalysis for environmental applications.

CRISPR-based high-throughput genome-wide loss-of-function screens are a valuable approach to functional genetics and strain engineering. The yeast Komagataella phaffii is a host of particular interest in the biopharmaceutical industry and as a metabolic engineering host for proteins and metabolites. Here, we design and validate a highly active 6-fold coverage genome-wide sgRNA library for this biotechnologically important yeast containing 30,848 active sgRNAs targeting over 99% of its coding sequences. Conducting fitness screens in the absence of functional non-homologous end joining (NHEJ), the dominant DNA repair mechanism in K. phaffii, provides a quantitative means to assess the activity of each sgRNA in the library. This approach allows for the experimental validation of each guide's targeting activity, leading to more precise screening outcomes. We used this approach to conduct growth screens with glucose as the sole carbon source and identify essential genes. Comparative analysis of the called gene sets identified a core set of K. phaffii essential genes, many of which relate to metabolic engineering targets, including protein production, secretion, and glycosylation. The high activity, genome-wide CRISPR library developed here enables functional genomic screening in K. phaffii, applied here to gene essentiality classification, and promises to enable other genetic screens.