Depositing manganese dioxide (MnO2) onto three-dimensional (3D) porous scaffolds—such as carbon lattices, nickel foam, or graphene frameworks—is a widely used strategy in developing high-performance electrochemical devices like supercapacitors, batteries, and catalysts.The primary objective is to build a hybrid structure that overcomes the natural material limitations of MnO2 while maximizing its high energy storage capabilities. But conventional MnO2 electrodeposition has a number of limitations. Conventional MnO2 electrodeposition tends to form thick coatings only on the outer surfaces of porous substrates. This excessive surface growth leads to pore blockage and poor penetration into the internal structure, impeding electrolyte access and reducing ion transport efficiency. This leads to low utilization of the available surface area and active material, severely limiting electrode performance. Achieving both high mass loading and coating uniformity remains a major challenge. Existing methods such as hydrothermal synthesis often suffer from inherently low loading (typically < 40 mg cm-2), which constrains device energy density and practicality. MnO2 nucleation is dominated by random and large island growth, resulting in thick, non-uniform films with thickness gradients that hinder charge and ion transfer kinetics.  Highly loaded electrodes prepared by conventional methods exhibit thick MnO2 coating often show poor electrochemical kinetics due to poor electron/ion conductivity.Finally, there is a lack of controlled interface regulation during electrodeposition.   

The aim of this work is to target the production of age-specific production of hyperactive platelets as a therapeutic platform to control clot formation that causes thrombosis, stroke, heart attacks, and other cardiovascular disease, as well as platelet overproduction disorders such as essential thrombocytosis. In particular, this effort specifically targets cells that have progressed down an age-specific differentiation pathway. These age specific platelets are hyperactive relative to platelets from younger progenitor cells. These older platelet progenitor cells have been characterized molecularly and functionally characterization and can be targeted using pharmacological, antibody-based, cell based or gene therapy based strategies to control clot formation and platelet activity and numbers.

The transition to 5G and 6G networks has led to a widespread adoption of machine learning (ML) for critical functions like modulation classification, channel estimation, resource management, and spectrum sensing. While ML has enhanced operational efficiency, it has simultaneously expanded the attack surface for adversarial ML at the Physical Layer (PHY), for example, from Generative Adversarial Networks (GANs). While techniques like radio frequency (RF) fingerprinting have emerged as a PHY-level authentication method based on hardware-induced signal traits (such as in-phase/quadrature (I/Q) imbalance and error vector magnitude), GANs can synthesize RF signals to mimic legitimate hardware-induced features up to 95% similarity. This is close enough to evade most detection schemes. Existing defenses to GANs based on convolutional neural networks, deep neural networks, supervised retraining, and/or heuristics do not generalize well across different modulations, protocols, channel conditions, or unseen attack types. Autoencoder and reconstruction-based approaches are often limited to clean reference signals, which are not always available in dynamic wireless environments. While GANs are excellent at mimicking low-order statistics (mean/variance), they fail to replicate complex signal structures.

Methionine (Met) is a common amino acid found in almost all proteins. When it undergoes oxidation (a common process in aging and disease), it transforms into methionine sulfoxide (Met-SO).The challenge is that this chemical reaction creates a new chiral center at the sulfur atom. This means that for every oxidized methionine, two different mirror-image versions (diastereomers) can exist: The (S,S) form and the (S,R) form.Before this invention, researchers struggled to separate these two forms. This resulted in two major technical hurdles:Standard techniques like High Performance Liquid Chromatography (HPLC) or fractional crystallization (a method dating back to 1947) were unreliable, difficult to reproduce, and failed to produce high-purity samplesBecause the two forms were so difficult to separate, almost all previous research on methionine oxidation used a mixture of both. This meant that if one form was toxic and the other was harmless, the results would be averaged out, hiding the true biological mechanism.A core motivation for this invention is the "staggering degree of disagreement" in Alzheimer's Disease research regarding the protein Amyloid beta (Aβ42)Some studies claimed that oxidized Aβ42 increased brain plaque toxicity, while others claimed it decreased itIt is plausible that these contradictions exist because previous researchers didn't know which specific diastereomer—(S,S) or (S,R)—they were testinOnce these two forms are created, they are remarkably stable. The energy barrier to flip from one form to the other is roughly 45.2 kcal/mol, which is significantly higher than other enantiomeric structures. This means that in the human body, the "wrong" version won't just flip back to the "right" one; it stays in that specific shape, potentially causing long-term damage if not properly regulated by specific enzymes (reductases). 

Comprehensive radiation detection across the spectral range requires distinct systems for ionizing and non-ionizing imaging because each technology faces unique architectural hurdles. Modern visible light detection has successfully transitioned from passive plates to digital Active Pixel Sensors (APS) by leveraging Complementary Metal-Oxide-Semiconductor (CMOS) technology to provide every pixel with its own dedicated amplifier and active circuitry. Ionizing radiation detection like X-ray and gamma-ray has relied on exotic scintillators to convert radiation into light, a process prone to lateral light scattering and degraded spatial resolution. Recent advancements in ionizing radiation have shifted toward direct conversion materials like amorphous selenium (a-Se), which transform X-rays directly into electrical charges. However, these direct-conversion devices do not scale to larger areas without significant noise being a factor. This is primarily due to thin-film transistor (TFT) backplanes which, unlike their CMOS counterparts, lack the local amplification necessary to maintain a high signal-to-noise ratio.

Machine learning has transitioned from traditional supervised learning to more resource-efficient sketching and federated techniques. Early compressive learning relied on hand-crafted random projections and task-specific iterative solvers. While these methods reduced data volume, they were inflexible because a change in data distribution or task required a complete redesign of the projection. Concurrently, privacy-preserving needs led to the rise of federated learning and differential privacy. However, these methods often struggled with high communication costs and the inability to merge model updates effectively across different architectures. Until recently, the state of the art remained fairly bifurcated, where one could have either high-accuracy iterative training on raw data, or efficient but brittle and task-specific compressed representations that lacked generalizability across diverse analytical tasks, e.g., Principal Component Analysis (PCA), regression, clustering.

The recent state of the art in network routing has been dominated by the Border Gateway Protocol (BGP). While BGP is the standard for inter-domain routing, it primarily relies on single-path propagation based on shortest-path or policy-driven criteria and metrics. Traditional multi-path approaches to network routing are challenged by routing loops and slow convergence in changing network topology environments. More recently, Software Defined Networking (SDN) and Segment Routing have attempted to provide more granular control; however, ensuring loop-free paths across multiple autonomous systems without impractical overhead remains a stubborn issue. In considering larger-scale network communications involving internet protocols (IP), these modern networks require higher resilience and bandwidth, making the ability to utilize multiple paths simultaneously without the risk of circular routing increasingly desirable.

Monitoring of viral infections such as with the SARS-CoV-2 virus was vital to detection and characterization of new variants before they became widespread and allowed public health agencies to deploy resources and develop policies in advance of new waves of the virus. I The ARTIC Network developed a panel of primers and a workflow for whole genome sequencing of SARS-CoV-2 using multiplex PCR. This became a popular strategy for sequencing. The ARTIC protocol generates overlapping PCR amplicons that span the SARS-CoV-2 genome using a defined multiplex PCR primer set. These were sequenced and mapped to the SARS-CoV-2 genome to generate a high quality consensus sequence of the variant in the sample. While ARTIC was developed for SARS-CoV-2, the protocol is readily adaptable to a wide array of viruses. Despite its clear utility, challenges arose for ARTIC: new variants would arise that the consensus primers would not recognize and all testing for those new variants would be compromised. Normalization of samples with high variation of starting template proved difficult and sequencing library preparation was not optimized for convenience, speed, or cost.