Louisiana Tech University

ABSTRACT Adoption of additively manufactured electronics for low and high‐volume manufacturing is hindered by available inks and printing variations. Achieving repeatable and consistent features on flexible substrates remains challenging due to ink formulation and process limitations. Herein, we report a ternary‐solvent formulation of 2D graphene‐based ink, optimized to control volatility, surface tension, and viscosity for stable ultrasonic atomization. By optimizing process parameters such as nozzle size, focusing ratio, and platen temperature, we demonstrate high‐resolution printing using an aerosol jet printer (AJP), achieving feature sizes down to 5 µm in the as‐printed state, where linewidths are defined directly by the printing process without post‐print geometric refinement. Thermal annealing is subsequently employed to enhance conductivity. To demonstrate AJP's process flexibility for fabricating functional devices, interdigitated electrodes (IDEs) for capacitive touch sensing and serpentine structures for Joule heating were printed. The printed IDEs operate without dielectric coatings and exhibited consistent capacitive response (∼2 pF) to direct touch stimuli, while the heater demonstrated uniform and tunable thermal output (up to ∼56°C) under low‐power operation (150 mW). These results demonstrate an ink formulation that enables high‐resolution patterning and highlights AJP's potential as a scalable, lithography‐free platform utilizing solution‐processed 2D materials for electronics and sensors.

Modern information systems can be subjected to malicious attacks from the compromise of a valid user’s account credentials, or even from the malicious use of an internal authorized user often referred to as an insider. These types of attacks can be extremely difficult to detect especially when valid user credentials are being utilized. This paper reviews a range of literature on a variety of user account compromise and user behavior anomaly detection techniques. The methods proposed in the literature covered here include both supervised and unsupervised machine learning approaches. Much research is being performed to bring reliable anomalous user detection capabilities to today’s organizations.

Head and neck squamous cell carcinomas (HNSCCs) are considered the most common histological type of head and neck cancer. This study aims to develop a drug delivery system based on zein protein nanoparticles (Zein NPs) to enhance the therapeutic effect of the anticancer peptide, Pistacia zardin1 (PZ1), for the treatment of maxillofacial cancers. PZ1-Zein NPs were synthesized by the desolvation method. These spherical nanoparticles (size: 162.8 nm, PDI: 0.27) showed high encapsulation efficiency (89%) and pH-responsive release (with higher drug release in the acidic tumor microenvironment). In vitro cytotoxicity assays showed that PZ1-Zein NPs significantly reduced IC50 values in HNSCC cell lines (e.g., SCC-25: 7.5 µM vs. 19.3 µM for free peptide, p < 0.001) while exhibiting improved selectivity for cancer cells over normal HaCaT cells. Mechanistic investigations confirmed that PZ1-Zein NPs significantly increased apoptosis, as shown by increased caspase-3/7 activity (5.8-fold vs. 2.6-fold). These findings highlight PZ1-Zein NPs as a promising nanomedicine strategy and a candidate functional component for future dual-functional scaffolds aimed at targeted hard tissue engineering and surgery in HNSCC management.

Determine animal performance and eco-nomic net returns for 6 bermudagrass-based pasture graz-ing systems in the US Southern Great Plains. Animal performance data from a 4-yr split-plot grazing study conducted in south-central Oklahoma were used to evaluate 6 bermudagrass-based stocker cattle grazing systems: bermudagrass forage with 112 kg N/ha (100 lb/acre) and a feed supplement under continuous stocking (BNSC), bermudagrass forage with 112 kg N/ha and a feed supplement grazed in a 28-d rest rotation (BNSR), bermudagrass forage without N fer-tilizer with a feed supplement under continuous stocking (BSC), bermudagrass forage without N fertilizer with a feed supplement grazed in a 28-d rest rotation (BSR), ber-mudagrass forage interseeded with grazing-tolerant alfalfa under continuous stocking (BAC), and bermudagrass for-age interseeded with grazing-tolerant alfalfa grazed in a 28-d rest rotation (BAR). Enterprise budgets and mixed effect models were used to estimate means for measures of animal performance and net returns. Total gain was greater in the BNSC system (324 kg/ha) than the BNSR and BAR sys-tems with gains of 307 and 299 kg/ha. Interseeded alfalfa with rotational grazing (BAR) had the greatest net re-turn of $44/ha ($18/acre). The next-best system was BAC with a net return of −$56/ha. The conventional systems (BNSC and BNSR) had low net returns due to fertilizer and supplemental feed costs, and rotational systems (BSR and BNSR) had low net returns due to greater water, fenc-ing, and labor costs. The bermudagrass systems with interseeded alfalfa performed better eco-nomically than the conventional bermudagrass systems. The alfalfa rotational system was more economical than conventional continuously stocked systems because giving alfalfa time to recover from preferential grazing, extended its stand life.

Interfacial defects remain a key limitation of the performance of perovskite solar cells (PSCs). Using first-principles density functional theory (DFT) and SCAPS-1D device simulations, we examine how sulfonate and primary amine salts can jointly passivate the SnO2/perovskite buried interface. The sulfonate group forms strong S-O-Sn bonds with surface Sn atoms, while the ammonium cation anchors to undercoordinated Pb2+ through N-H···I interactions, creating a complementary interfacial dipole and lowering the trap density. Calculated adsorption energies up to -4.53 eV confirm robust binding and efficient defect deactivation. Device simulations calibrated to experimental parameters yield an initial efficiency of 20.95% and increase to 28.8% after optimizing thicknesses and defect densities. Efficiencies above 30% arise in the near-ideal trap-free regime and represent theoretical upper limits for this architecture. Overall, the combined DFT and SCAPS analysis shows that cooperative sulfonate-amine passivation effectively mitigates non-radiative recombination and provides molecular guidelines for improving buried interfaces.

BackgroundAdaptive implementation strategies tailor support to setting needs rather than applying a uniform approach. These strategies improve efficiency and fit, yet practical guidance on identifying decision points and tailoring variables is limited. This study collected end-user and partner input to specify decision points and tailoring variables for an adaptive implementation strategy.MethodsThis study focused on the evidence-based nutrition program, Together, We Inspire Smart Eating (WISE). End users and implementation partners with prior experience in WISE were recruited in two states to participate in semi-structured interviews or focus groups designed to elicit feedback to specify an adaptive implementation strategy for WISE.ResultsQualitative input supported three crucial decisions for an adaptive implementation strategy: (1) low-intensity support, the starting point for all sites, will include leadership commitments, local champions, an implementation blueprint, classroom reminders, and task-focused facilitation at the site level; (2) assessment of response to low-intensity support will occur in October (Month 3) of the school year; and (3) sites not responding by Month 3 will receive holistic facilitation and tailored educational materials at the teacher level. Participants emphasized the universal need for facilitation at all sites, with struggling sites requiring more. They also identified tailoring variables: sites with fewer than 60% of classrooms achieving fidelity would require high-intensity support.ConclusionsThis study illustrates a process for using feedback from end users and partners to define key elements of an adaptive implementation strategy. Our approach holds significant potential to specify strategies for scaling health-related evidence.

Abstract This study investigates the impact of infill print direction on the mechanical and dynamic properties of 3D printed filaments composed of Acrylonitrile Butadiene Styrene (ABS) and Ground Tire Rubber (GTR). As the recycling of waste tires becomes increasingly important for environmental sustainability, GTR-ABS composites offer a promising alternative for vibration-damping applications. Filaments were fabricated with varying GTR content (0%, 20%, and 40%) and printed with infill orientation increments of 15°, 30°, and 90°. Mechanical testing, including tensile strength analysis, and dynamic testing, including vibration assessment, were conducted to evaluate the effects of print angle and filler concentration. Results showed that higher GTR content significantly reduced tensile strength. However, dynamic properties such as frequency showed a nonlinear relationship for 20% GTR at 15° and 30° angle of print. These findings underscore the critical role of print orientation and filler content in tailoring performance of sustainable composite materials for specific engineering applications where damping and vibration control of polymers is an important consideration.

Abstract Cu-Ni-Cr alloys—composed of copper, nickel, and chromium—are widely valued for their exceptional combination of properties and are used across multiple sectors including marine, aerospace, and electronics. Their durability in corrosive environments, efficient thermal and electrical conductance, and strong mechanical performance make them well-suited for extreme conditions such as exposure to seawater and elevated temperatures. In this work, an Integrated Computational Materials Engineering (ICME) approach, combining Molecular Dynamics (MD) simulations with mesoscale Finite Element (FE) modeling, is proposed for optimizing the composition of the Cu-Ni-Cr alloy to enhance its mechanical performance. The MD simulations offer atomistic insight into stress-strain responses and provide key input parameters—such as elastic constants and yield behavior for the FE models. The FE simulations then predict how compositional variations influence mechanical performance under practical loading conditions. The study identifies the major limitation of such approach, which is a mismatch between the material properties obtained from simulations and those from experiments. Once such limitation is overcome, the integrated framework enables an efficient approach for predicting compositions with superior properties for applications in automotive, aerospace, and advanced manufacturing environments.

Abstract Implantable neural interfaces require devices capable of long-term electrical and chemical monitoring with high durability and reliability. Our group has previously developed fully glassy carbon microelectrode arrays (“all”-GC-MEAs), in which both electrodes and interconnects are composed of homogeneous GC material integrated on flexible substrates. This design eliminates metal interconnects, improving mechanical resilience and long-term electrical performance under chronic stress. To enhance the microfabrication of “all”-GC-MEAs and explore novel material integration strategies, this study focuses on the electrical and structural properties of GC as an interconnect material. We systematically examine the sheet resistance of GC films at varying pyrolyzed thicknesses and investigate the impact of a secondary pyrolysis process. Raman spectroscopy is employed to assess graphitization, structural order, and defect density of the GC films. Our overarching aim is to support further miniaturization of “all”-GC-MEAs while maintaining high electrical conductivity, electrochemical sensitivity, and device flexibility. Additionally, we compare the serotonin (5-HT) sensing performance of microelectrodes processed through one versus two pyrolysis cycles using fast-scan cyclic voltammetry, to evaluate their electrochemical efficacy.

The modern web stack, which is dominated by browser-based applications and API-first backends, now operates under an adversarial equilibrium where automated, AI-assisted attacks evolve continuously. Content Delivery Networks (CDNs) and edge computing place programmable defenses closest to users and bots, making them natural enforcement points for machine-learning (ML) driven inspection, throttling, and isolation. This survey synthesizes the landscape of AI-enhanced defenses deployed at the edge: (i) anomaly-and behavior-based Web Application Firewalls (WAFs) within broader Web Application and API Protection (WAAP), (ii) adaptive DDoS detection and mitigation, (iii) bot management that resists human-mimicry, and (iv) API discovery, positive security modeling, and encrypted-traffic anomaly analysis. We add a systematic survey method, a threat taxonomy mapped to edge-observable signals, evaluation metrics, deployment playbooks, and governance guidance. We conclude with a research agenda spanning XAI, adversarial robustness, and autonomous multi-agent defense. Our findings indicate that edge-centric AI measurably improves time-to-detect and time-to-mitigate while reducing data movement and enhancing compliance, yet introduces new risks around model abuse, poisoning, and governance.