Genomic Evidence for the Complex Evolutionary History of Macaques (Genus Macaca)

The genus Macaca is widely distributed, occupies a variety of habitats, shows diverse phenotypic characteristics, and is one of the best-studied genera of nonhuman primates. Here, we reported five re-sequencing Macaca genomes, including one M. cyclopis, one M. fuscata, one M. thibetana, one M. silenus, and one M. sylvanus. Together with published genomes of other macaque species, we combined 20 genome sequences of 10 macaque species to investigate the gene introgression and genetic differences among the species. The network analysis of the SNV-fragment trees indicates a reticular phylogeny of macaque species. Combining the results from various analytical methods, we identified extensive ancient introgression events among macaque species. The multiple introgression signals between different species groups were also observed, such as between fascicularis group species and silenus group species. However, gene flow signals between fascicularis and sinica group were not as strong as those between fascicularis group and silenus group. On the other hand, the unidirect gene flow in M. arctoides probably occurred between the progenitor of M. arctoides and the common ancestor of fascicularis group. Our study also shows that the genetic backgrounds and genetic diversity of different macaques vary dramatically among species, even among populations of the same species. In conclusion, using whole genome sequences and multiple methods, we have studied the evolutionary history of the genus Macaca and provided evidence for extensive introgression among the species.

Large-Area Fabrication of Porous Graphene Networks on Carbon Fabric via Millisecond Photothermal Processing of Polyaniline for Supercapacitors

Supercapacitors demonstrate promising potential for flexible, multi-functional energy storage devices; however, their widespread adoption is confronted by fabrication challenges. To access a combination of desirable device qualities such as flexibility, lightweight, structural stability, and enhanced electrochemical performance, carbon fiber (CF) can be utilized as a current collector, alongside graphene as an electrochemically active material. Yet achieving a cost-effective, large-scale graphene production, particularly on CF, remains challenging. Here, a rapid (<1 min) photothermal approach is developed for the large-scale production of graphene directly onto CF, utilizing polyaniline (PANI) as a polymer precursor. The in situ electropolymerization of PANI on CF facilitates its rapid synthesis on large areas, followed by conversion into graphene networks, enabling the binder-free fabrication of supercapacitor devices. These devices exhibit an areal capacitance of 180 mF cm-2 (at 2 mA cm-2 in 1 m H2SO4), an order of magnitude higher than other fabric-based devices. Moreover, the devised photothermal strategy allows for one-step preparation of supercapacitor devices on areas exceeding 100 cm-2, yielding an absolute areal capacitance of 4.5 F. The proportional increase in capacitance with device area facilitates scaling and indicates the commercial viability of this approach for low-cost, energy-efficient, and high-throughput production of lightweight, high-performance graphene-based multi-functional supercapacitor devices.

Complexity synchronization in emergent intelligence

In this work, we use a simple multi-agent-based-model (MABM) of a social network, implementing selfish algorithm (SA) agents, to create an adaptive environment and show, using a modified diffusion entropy analysis (DEA), that the mutual-adaptive interaction between the parts of such a network manifests complexity synchronization (CS). CS has been shown to exist by processing simultaneously measured time series from among organ-networks (ONs) of the brain (neurophysiology), lungs (respiration), and heart (cardiovascular reactivity) and to be explained theoretically as a synchronization of the multifractal dimension (MFD) scaling parameters characterizing each time series. Herein, we find the same kind of CS in the emergent intelligence of groups formed in a self-organized social interaction without macroscopic control but with biased self-interest between two groups of agents playing an anti-coordination game. This computational result strongly suggests the existence of the same CS in real-world social phenomena and in human-machine interactions as that found empirically in ONs.

From Cognitive Agents to Cognitive Systems: Theoretical, Methodological, and Empirical Developments of van Gelder's (1998) "Dynamical Hypothesis"

Over two decades have passed since the publication of van Gelder's (1998) "dynamical hypothesis." In that paper, van Gelder proposed that cognitive agents were not digital computers-per the representational computational approach-but dynamical systems. The evolution of the dynamical hypothesis was driven by parallel advances in three areas. Theoretically, a deeper understanding of genetics, biology, neuroscience, and cognitive science inspired questions about how systems within each domain dynamically interact and extend their effects across spatiotemporal scales. Methodologically, more sophisticated and domain-general tools allowed researchers to discover, model, and quantify system dynamics, structure, and patterns across multiple scales to generate a more comprehensive system-level understanding of behaviors. Empirically, we can analyze a system's behavior while preserving its natural dynamics, revealing evidence that the reductionist approach leads to an incomplete understanding of the components and the overall system. Researchers have traditionally reduced a complex system into its component processes and assumed that the parts can be recombined to explain the whole. These three advances fundamentally altered our understanding of a "cognitive agent:" How their behaviors are driven by long-range coordination across multiple processes, how the interdependent and nested structure of interacting variables produces behaviors that are greater than the sum of its parts, and how environmental constraints shape adaptive yet stable behavioral patterns.

Interpretable Performance Models for Energetic Materials using Parsimonious Neural Networks

Predictive models for the performance of explosives and propellants are important for their design, optimization, and safety. Thermochemical codes can predict some of these properties from fundamental quantities such as density and formation energies that can be obtained from first principles. Models that are simpler to evaluate are desirable for efficient, rapid screening of material screening. In addition, interpretable models can provide insight into the physics and chemistry of these materials that could be useful to direct new synthesis. Current state-of-the-art performance models are based on either the parametrization of physics-based expressions or data-driven approaches with minimal interpretability. We use parsimonious neural networks (PNNs) to discover interpretable models for the specific impulse of propellants and detonation velocity and pressure for explosives using data collected from the open literature. A combination of evolutionary optimization with custom neural networks explores and trains models with objective functions that balance accuracy and complexity. For all three properties of interest, we find interpretable models that are Pareto optimal in the accuracy and simplicity space.

Simultaneous High-Frame-Rate Acoustic Plane-Wave and Optical Imaging of Intracranial Cavitation in Polyacrylamide Brain Phantoms during Blunt Force Impact

Blunt and blast impacts occur in civilian and military personnel, resulting in traumatic brain injuries necessitating a complete understanding of damage mechanisms and protective equipment design. However, the inability to monitor in vivo brain deformation and potential harmful cavitation events during collisions limits the investigation of injury mechanisms. To study the cavitation potential, we developed a full-scale human head phantom with features that allow a direct optical and acoustic observation at high frame rates during blunt impacts. The phantom consists of a transparent polyacrylamide material sealed with fluid in a 3D-printed skull where windows are integrated for data acquisition. The model has similar mechanical properties to brain tissue and includes simplified yet key anatomical features. Optical imaging indicated reproducible cavitation events above a threshold impact energy and localized cavitation to the fluid of the central sulcus, which appeared as high-intensity regions in acoustic images. An acoustic spectral analysis detected cavitation as harmonic and broadband signals that were mapped onto a reconstructed acoustic frame. Small bubbles trapped during phantom fabrication resulted in cavitation artifacts, which remain the largest challenge of the study. Ultimately, acoustic imaging demonstrated the potential to be a stand-alone tool, allowing observations at depth, where optical techniques are limited.

High-throughput determination of grain size distributions by EBSD with low-discrepancy sampling

Because microstructure plays an important role in the mechanical properties of structural materials, developing the capability to quantify microstructures rapidly is important to enabling high-throughput screening of structural materials. Electron backscatter diffraction (EBSD) is a common method for studying microstructures and extracting information such as grain size distributions (GSDs), but is not particularly fast and thus could be a bottleneck in high-throughput systems. One approach to accelerating EBSD is to reduce the number of points that must be scanned. In this work, we describe an iterative method for reducing the number of scan points needed to measure GSDs using incremental low-discrepancy sampling, including on-the-fly grain size calculations and a convergence test for the resulting GSD based on the Kolmogorov-Smirnov test. We demonstrate this method on five real EBSD maps collected from magnesium AZ31B specimens and compare the effectiveness of sampling according to two different low discrepancy sequences, the Sobol and R2 sequences, and random sampling. We find that R2 sampling is able to produce GSDs that are statistically very similar to the GSDs of the full density grids using, on average, only 52% of the total scan points. For EBSD maps that contained monodisperse GSDs and over 1000 grains, R2 sampling only required an average of 39% of the total EBSD points.

Standard experimental paradigm designs and data exclusion practices in cognitive psychology can inadvertently introduce systematic "shadow" biases in participant samples

Standard cognitive psychology research practices can introduce inadvertent sampling biases that reduce the reliability and generalizability of the findings. Researchers commonly acknowledge and understand that any given study sample is not perfectly generalizable, especially when implementing typical experimental constraints (e.g., limiting recruitment to specific age ranges or to individuals with normal color vision). However, less obvious systematic sampling constraints, referred to here as "shadow" biases, can be unintentionally introduced and can easily go unnoticed. For example, many standard cognitive psychology study designs involve lengthy and tedious experiments with simple, repetitive stimuli. Such testing environments may 1) be aversive to some would-be participants (e.g., those high in certain neurodivergent symptoms) who may self-select not to enroll in such studies, or 2) contribute to participant attrition, both of which reduce the sample's representativeness. Likewise, standard performance-based data exclusion efforts (e.g., minimum accuracy or response time) or attention checks can systematically remove data from participants from subsets of the population (e.g., those low in conscientiousness). This commentary focuses on the theoretical and practical issues behind these non-obvious and often unacknowledged "shadow" biases, offers a simple illustration with real data as a proof of concept of how applying attention checks can systematically skew latent/hidden variables in the included population, and then discusses the broader implications with suggestions for how to manage and reduce, or at a minimum acknowledge, the problem.

The effects of temperature on elastic energy storage and release in a system with a dynamic mechanical advantage latch

Changes in temperature alter muscle kinetics and in turn affect whole-organism performance. Some organisms use the elastic recoil of biological springs, structures which are far less temperature sensitive, to power thermally robust movements. For jumping frogs, the use of elastic energy in tendons is facilitated through a geometric latching mechanism that operates through dynamic changes in the mechanical advantage (MA) of the hindlimb. Despite the well-documented use of elastic energy storage, frog jumping is a locomotor behavior that is significantly affected by changes in temperature. Here, we used an in vitro muscle preparation interacting in real time with an in silico model of a legged jumper to understand how changes in temperature affect the flow of energy in a system using a MA latch. We used the plantaris longus muscle-tendon unit (MTU) to power a virtual limb with changing MA and a mass being accelerated through a real-time feedback controller. We quantified the amount of energy stored in and recovered from elastic structures and the additional contribution of direct muscle work after unlatching. We found that temperature altered the duration of the energy loading and recovery phase of the in vitro/in silico experiments. We found that the early phase of loading was insensitive to changes in temperature. However, an increase in temperature did increase the rate of force development, which in turn allowed for increased energy storage in the second phase of loading. We also found that the contribution of direct muscle work after unlatching was substantial and increased significantly with temperature. Our results show that the thermal robustness achieved by an elastic mechanism depends strongly on the nature of the latch that mediates energy flow, and that the relative contribution of elastic and direct muscle energy likely shapes the thermal sensitivity of locomotor systems.

Selective and Scalable Control of Spin Quantum Memories in a Photonic Circuit

A central goal in many quantum information processing applications is a network of quantum memories that can be entangled with each other while being individually controlled and measured with high fidelity. This goal has motivated the development of programmable photonic integrated circuits (PICs) with integrated spin quantum memories using diamond color center spin-photon interfaces. However, this approach introduces a challenge into the microwave control of individual spins within closely packed registers. Here, we present a quantum memory-integrated photonics platform capable of (i) the integration of multiple diamond color center spins into a cryogenically compatible, high-speed programmable PIC platform, (ii) selective manipulation of individual spin qubits addressed via tunable magnetic field gradients, and (iii) simultaneous control of qubits using numerically optimized microwave pulse shaping. The combination of localized optical control, enabled by the PIC platform, together with selective spin manipulation opens the path to scalable quantum networks on intrachip and interchip platforms.

Assessment of a Perfusion and Ventilation Method for Detecting Lung and Liver Injury in a Cadaveric Model

Surgical simulation models have been developed using post-mortem human subjects (PMHS). These models involve the pressurization and ventilation of the PMHS to create a more realistic environment for training and the practice of surgical procedures. The overall objective of this study was to determine the feasibility of a previously developed surgical simulation model to detect soft tissue injuries during a ballistic impact to the torso. One of the main limitations of using PMHS for the assessment of soft tissue injuries in the field of injury biomechanics is the lack of physiological blood flow. To overcome this limitation, the assessment of the surgical simulation model for use in injury biomechanics applications was conducted based on data collected from behind armor blunt trauma (BABT) case studies. Documented injuries in real-world cases included anterior lung contusion, posterior lung contusion, and liver contusion. These real-world injuries were compared to those seen post-impact in the PMHS using pathological and histological techniques. Discussion of limitations and future work is presented.

Great minds think alike: New measures to quantify the similarity of recalls

Given the recent interest in how memory operates in social contexts, it is more important than ever to meaningfully measure the similarity between recall sequences of different individuals. Similarity of recall sequences of different individuals has been quantified using primarily order-agnostic and some order-sensitive measures specific to memory research without agreement on any one preferred measure. However, edit distance measures have not been used to quantify the similarity of recall sequences in collaborative memory studies. In the current study, we review a broad range of similarity measures, highlighting commonalities and differences. Using simulations and behavioral data, we show that edit distances do measure a memory-relevant factor of similarity and capture information distinct from that captured by order-agnostic measures. We answer illustrative research questions which demonstrate potential applications of edit distances in collaborative and individual memory settings and reveal the unique impact collaboration has on similarity.

Evaluation of U.S. Army Soldiers wearing a back exosuit during a field training exercise

Back overuse injuries are a significant problem in the U.S. Army, responsible for nearly a quarter of musculoskeletal injuries. Back exosuits are wearable devices that relieve musculoskeletal strain, make lifting easier, and could potentially reduce Soldier overuse injuries. But published studies have not evaluated exosuits during realistic field operations to assess acceptability to Soldiers. We tested a back exosuit on field artillery Soldiers during a field training exercise. Afterward, Soldiers completed a survey to quantify their satisfaction, intent to use, and performance impact of the exosuit. Feedback was overwhelmingly positive: Approximately 90% of Soldiers reported that exosuits increased their ability to perform their duties, and 100% said that if the exosuit were further developed and made available to them, they would be likely to wear it. These numerical survey results indicated that exosuits can provide a practical and acceptable way to assist lifting and augment physical performance during realistic Army operations without interfering with other duties.

Active search signatures in a free-viewing task exploiting concurrent EEG and eye movements recordings

Tasks we often perform in our everyday lives, such as reading or looking for a friend in the crowd, are seemingly straightforward but they actually require the orchestrated activity of several cognitive processes. Free-viewing visual search requires a plan to move our gaze on the different items, identifying them, and deciding on whether to continue with the search. Little is known about the electrophysiological signatures of these processes in free-viewing because there are technical challenges associated with eye movement artefacts. Here, we aimed to study how category information, as well as ecologically relevant variables such as the task performed, influence brain activity in a free-viewing paradigm. Participants were asked to observe/search from an array of faces and objects embedded in random noise. We concurrently recorded electroencephalogram and eye movements and applied a deconvolution analysis approach to estimate the contribution of the different elements embedded in the task. Consistent with classical fixed-gaze experiments and a handful of free-viewing studies, we found a robust categorical effect around 150 ms in occipital and occipitotemporal electrodes. We also report a task effect, more negative in posterior central electrodes in visual search compared with exploration, starting at around 80 ms. We also found significant effects of trial progression and an interaction with the task effect. Overall, these results generalise the characterisation of early visual face processing to a wider range of experiments and show how a suitable analysis approach allows to discern among multiple neural contributions to the signal, preserving key attributes of real-world tasks.

Empowering human-AI teams via Intentional Behavioral Synchrony

As Artificial Intelligence (AI) proliferates across various sectors such as healthcare, transportation, energy, and military applications, the collaboration between human-AI teams is becoming increasingly critical. Understanding the interrelationships between system elements - humans and AI - is vital to achieving the best outcomes within individual team members' capabilities. This is also crucial in designing better AI algorithms and finding favored scenarios for joint AI-human missions that capitalize on the unique capabilities of both elements. In this conceptual study, we introduce Intentional Behavioral Synchrony (IBS) as a synchronization mechanism between humans and AI to set up a trusting relationship without compromising mission goals. IBS aims to create a sense of similarity between AI decisions and human expectations, drawing on psychological concepts that can be integrated into AI algorithms. We also discuss the potential of using multimodal fusion to set up a feedback loop between the two partners. Our aim with this work is to start a research trend centered on exploring innovative ways of deploying synchrony between teams of non-human members. Our goal is to foster a better sense of collaboration and trust between humans and AI, resulting in more effective joint missions.

Distrust as a form of inequality

Navigating social hierarchies is a ubiquitous aspect of human life. Social status shapes our thoughts, feelings, and actions toward others in various ways. However, it remains unclear how trust is conferred within hierarchies and how status-related cues are used when resources are on the line. This research fills this knowledge gap by examining how ascribed, consensus-based status appearance, and perceived status appearance impact investment decisions for high- and low-status partners during a Trust Game. In a series of pre-registered experiments, we examined the degree to which participants trusted unfamiliar others with financial investments when the only available information about that person was their socioeconomic status (SES). In Study 1, SES was ascribed. Studies 2 and 3 conveyed SES with visual antecedents (clothing). Across all three experiments, participants trusted high SES partners more than low SES partners. In addition, subjective perceptions of status based on visual cues were a stronger predictor of trust than consensus-based status judgments. This work highlights a high status-trust bias for decisions where an individual's money is on the line. In addition, high-status trust bias may occur simply because of an individual's subjective assumptions about another's rank.

Anatomy of nanomagnetic switching at a 3D topological insulator PN junction

A P-N junction engineered within a Dirac cone system acts as a gate tunable angular filter based on Klein tunneling. For a 3D topological insulator with a substantial bandgap, such a filter can produce a charge-to-spin conversion due to the dual effects of spin-momentum locking and momentum filtering. We analyze how spins filtered at an in-plane topological insulator PN junction (TIPNJ) interact with a nanomagnet, and argue that the intrinsic charge-to-spin conversion does not translate to an external gain if the nanomagnet also acts as the source contact. Regardless of the nanomagnet's position, the spin torque generated on the TIPNJ is limited by its surface current density, which in turn is limited by the bulk bandgap. Using quantum kinetic models, we calculated the spatially varying spin potential and quantified the localization of the current versus the applied bias. Additionally, with the magnetodynamic simulation of a soft magnet, we show that the PN junction can offer a critical gate tunability in the switching probability of the nanomagnet, with potential applications in probabilistic neuromorphic computing.

Quantum Dot-Doped Electrospun Polymer Fibers for Explosive Vapor Sensors

This research seeks to support reconnaissance efforts against homemade explosives (HMEs) and improvised explosive devices (IEDs), which are leading causes of combat casualties in recent conflicts. The successful deployment of a passive sensor to be developed for first responders and military must take expense, training requirements, and physical burden all into consideration. By harnessing the size-dependent luminescence of quantum dots (QDs) being electrospun into polymer fibers, the authors of this work hope to progress toward the development of lightweight, multivariable, inexpensive, easy to use and interpret, field-applicable sensors capable of detecting explosive vapors. The data demonstrate that poly(methyl methacrylate) (PMMA), polystyrene (PS), and polyvinyl chloride (PVC) fibers doped with Fort Orange cadmium selenide (CdSe) QDs, Birch Yellow CdSe QDs, or carbon (C) QDs will quench in the presence of explosive vapors (DNT, TNT, TATP, and RDX). In all cases, the fluorescent signal of the doped fiber continuously quenched upon sustained exposure to the headspace vapors. The simple method for the integration of QDs into the fibers' structure combined with their straightforward visual response, reusability, and durability all present characteristics desired for a field-operable and multimodal sensor with the ability to detect explosive threats.

Temporal complexity measure of reaction time series: Operational versus event time

INTRODUCTION

Detrended fluctuation analysis (DFA) is a well-established method to evaluate scaling indices of time series, which categorize the dynamics of complex systems. In the literature, DFA has been used to study the fluctuations of reaction time Y(n) time series, where n is the trial number.

METHODS

Herein we propose treating each reaction time as a duration time that changes the representation from operational (trial number) time n to event (temporal) time t, or X(t). The DFA algorithm was then applied to the X(t) time series to evaluate scaling indices. The dataset analyzed is based on a Go-NoGo shooting task that was performed by 30 participants under low and high time-stress conditions in each of six repeated sessions over a 3-week period.

RESULTS

This new perspective leads to quantitatively better results in (1) differentiating scaling indices between low versus high time-stress conditions and (2) predicting task performance outcomes.

CONCLUSION

We show that by changing from operational time to event time, the DFA allows discrimination of time-stress conditions and predicts performance outcomes.

Electromagnetic Radiation Effects on MgO-Based Magnetic Tunnel Junctions: A Review

Magnetic tunnel junctions (MTJs) have been widely utilized in sensitive sensors, magnetic memory, and logic gates due to their tunneling magnetoresistance. Moreover, these MTJ devices have promising potential for renewable energy generation and storage. Compared with Si-based devices, MTJs are more tolerant to electromagnetic radiation. In this review, we summarize the functionalities of MgO-based MTJ devices under different electromagnetic irradiation environments, with a focus on gamma-ray radiation. We explore the effects of these radiation exposures on the MgO tunnel barriers, magnetic layers, and interfaces to understand the origin of their tolerance. This review enhances our knowledge of the radiation tolerance of MgO-based MTJs, improves the design of these MgO-based MTJ devices with better tolerances, and provides information to minimize the risks of irradiation under various irradiation environments. This review starts with an introduction to MTJs and irradiation backgrounds, followed by the fundamental properties of MTJ materials, such as the MgO barrier and magnetic layers. Then, we review and discuss the MTJ materials and devices' radiation tolerances under different irradiation environments, including high-energy cosmic radiation, gamma-ray radiation, and lower-energy electromagnetic radiation (X-ray, UV-vis, infrared, microwave, and radiofrequency electromagnetic radiation). In conclusion, we summarize the radiation effects based on the published literature, which might benefit material design and protection.

Electronic Structure Calculations of Static Hyper(Polarizabilities) of Substrate-Supported Group-IV and -V Elemental Monolayers

The substrate-induced effects on the polarizability (α) and first dipole hyperpolarizability (β) of group-IV (i.e., graphene, silicene, germanene, stanene) and group-V (i.e., phosphorene, arsenene, antimonene, and bismuthene) elemental monolayer nanoflakes are investigated. Density functional theory calculations show that these monolayers are bound with varying degrees of interaction strength with the Ag(111) substrate surface. Calculated dipole moment and β values are zero for the centrosymmetric configurations of the pristine elemental monolayers. On the other hand, substrate-induced changes in the electronic densities at the interface lead to substantially enhanced values of β, making these materials attractive for applications in the next-generation photonic technologies at the nanoscale.

Estimating microstructural feature distributions from image data using a Bayesian framework

Many microstructural characterizations methods collect data on a regular pixelized grid. This method of discretization introduces a form of measurement error which can be shown to be proportional to the resolution at which they are collected. Intuitively, measurements made from low-resolution data are associated with higher error, but quantification of this error is typically not performed. This is reflected in international standards for measurements of grain size, which only provide a recommended minimum number of sample points per microstructural component to ensure each component is sufficiently resolved. In this work, a new method for quantifying the relative uncertainty of such pixelized measurements is presented. Using a Bayesian framework and simulated data collection on features collected from a Voronoi tessellation, the distribution of true geometric properties given a particular set of measurements is computed. This conditional feature distribution provides a quantitative estimate for the relative uncertainty associated with measurements made at difference resolutions. The approach is applied to measurements of size, aspect ratio and perimeter of given microstructural components. Size distributions are shown to be the least sensitive to sampling resolution, and evidence is presented which shows that the international standards provide an overly conservative minimum resolution for grain size measurement in microstructures represented by a Voronoi tessellation.

Identification of Microorganisms that Bind Specifically to Target Materials of Interest Using a Magnetophoretic Microfluidic Platform

Discovery of microorganisms and their relevant surface peptides that specifically bind to target materials of interest can be achieved through iterative biopanning-based screening of cellular libraries having high diversity. Recently, microfluidics-based biopanning methods have been developed and exploited to overcome the limitations of conventional methods where controlling the shear stress applied to remove cells that do not bind or only weakly bind to target surfaces is difficult and the overall experimental procedure is labor-intensive. Despite the advantages of such microfluidic methods and successful demonstration of their utility, these methods still require several rounds of iterative biopanning. In this work, a magnetophoretic microfluidic biopanning platform was developed to isolate microorganisms that bind to target materials of interest, which is gold in this case. To achieve this, gold-coated magnetic nanobeads, which only attached to microorganisms that exhibit high affinity to gold, were used. The platform was first utilized to screen a bacterial peptide display library, where only the cells with surface peptides that specifically bind to gold could be isolated by the high-gradient magnetic field generated within the microchannel, resulting in enrichment and isolation of many isolates with high affinity and high specificity toward gold even after only a single round of separation. The amino acid profile of the resulting isolates was analyzed to provide a better understanding of the distinctive attributes of peptides that contribute to their specific material-binding capabilities. Next, the microfluidic system was utilized to screen soil microbes, a rich source of extremely diverse microorganisms, successfully isolating many naturally occurring microorganisms that show strong and specific binding to gold. The results show that the developed microfluidic platform is a powerful screening tool for identifying microorganisms that specifically bind to a target material surface of interest, which can greatly accelerate the development of new peptide-driven biological materials and hybrid organic-inorganic materials.

Molecular Weight Controls Interactions between Plastic Deformation and Fracture in Cold Spray of Glassy Polymers

Polymer cold spray has gained considerable attention as a novel manufacturing process. A promising aspect of this technology involves the ability to deposit uniform polymer coatings without the requirements of solvent and/or high-temperature conditions. The present study investigates the interplay between shear instability, often considered to be the primary mechanism for bond formation, and fracture, as a secondary energy dissipation mechanism, collectively governing the deposition of glassy thermoplastics on similar and dissimilar substrates. A hybrid experimental-computational approach is utilized to explore the simultaneous effects of several interconnected phenomena, namely the particle-substrate relative deformability, molecular weights, and the resultant yielding versus fracture of polystyrene particles, examined herein as a model material system. The computational investigations are based on constitutive plasticity and damage equations determined and calibrated based on a statistical data mining approach applied to a wide collection of previously reported stress-strain and failure data. Results obtained herein demonstrate that the underlying adhesion mechanisms depend strongly on the molecular weight of the sprayed particles. It is also shown that although the plastic deformation and shear instability are still the primary bond formation mechanisms, the molecular-weight-dependent fracture of the sprayed glassy polymers is also a considerable phenomenon capable of significantly affecting the deposition process, especially in cases involving the cold spray of soft thermoplastics on hard substrates. The strong interplay between molecular-weight-dependent plastic yielding and fracture in the examined system emphasizes the importance of molecular weight as a critical variable in the cold spray of glassy polymers, also highlighting the possibility of process optimization by proper feedstock selection.

Grain shape effects in bed load sediment transport

Bed load sediment transport, in which wind or water flowing over a bed of sediment causes grains to roll or hop along the bed, is a critically important mechanism in contexts ranging from river restoration1 to planetary exploration2. Despite its widespread occurrence, predictions of bed load sediment flux are notoriously imprecise3,4. Many studies have focused on grain size variability5 as a source of uncertainty, but few have investigated the role of grain shape, even though shape has long been suspected to influence transport rates6. Here we show that grain shape can modify bed load transport rates by an amount comparable to the scatter in many sediment transport datasets4,7,8. We develop a theory that accounts for grain shape effects on fluid drag and granular friction and predicts that the onset and efficiency of transport depend on the coefficients of drag and bulk friction of the transported grains. Laboratory experiments confirm these predictions and reveal that the effect of grain shape on sediment transport can be difficult to intuit from the appearance of grains. We propose a shape-corrected sediment transport law that collapses our experimental measurements. Our results enable greater accuracy in predictions of sediment transport and help reconcile theories developed for spherical particles with the behaviour of natural sediment grains.

Low Power In-Memory Computation with Reciprocal Ferromagnet/Topological Insulator Heterostructures

The surface state of a 3D topological insulator (3DTI) is a spin-momentum locked conductive state, whose large spin hall angle can be used for the energy-efficient spin-orbit torque based switching of an overlying ferromagnet (FM). Conversely, the gated switching of the magnetization of a separate FM in or out of the TI surface plane can turn on and off the TI surface current. By exploiting this reciprocal behavior, we can use two FM/3DTI heterostructures to design an integrated 1-transistor 1-magnetic tunnel junction random access memory unit (1T1MTJ RAM) for an ultra low power Processing-in-Memory (PiM) architecture. Our calculation involves combining the Fokker-Planck equation with the Nonequilibrium Green Function (NEGF) based flow of conduction electrons and Landau-Lifshitz-Gilbert (LLG) based dynamics of magnetization. Our combined approach allows us to connect device performance metrics with underlying material parameters, which can guide proposed experimental and fabrication efforts.

A review of covetics - current understanding and future perspectives

Covetics are a novel class of metal-carbon composites traditionally fabricated in an induction furnace with high power electrical current in the liquid metal-carbon mixture. The electrical current facilitates chemical conversion of carbon feedstock into graphene-metal crystalline structures. We explore the synthesis mechanism and hypothesize that carbon-metal species, rather than purely-carbon ions, are the reactant species driving the covetic reaction. Experimental mechanical and electrical property characterization in aluminum, silver, and copper covetics demonstrates improved tensile, hardness, and conductivity of covetic metals over pure metal controls. The literature proves that significantly improved material properties are possible with homogeneously distributed graphitic carbon in metal. High resolution transmission electron microscopy shows stripe, multidirectional, and alternating carbon-metal plane lattice structure nanocarbon patterns for aluminum, copper, and silver covetics, respectively, as well as high- and low-carbon concentration regions. Covetic Raman spectra and theoretical calculations indicate characteristic graphene signatures and the possibility of aluminum-graphene and silver-graphene bonding. This review consolidates the current literature and provides new avenues for research.

Adaptive granularity in tensors: A quest for interpretable structure

Data collected at very frequent intervals is usually extremely sparse and has no structure that is exploitable by modern tensor decomposition algorithms. Thus, the utility of such tensors is low, in terms of the amount of interpretable and exploitable structure that one can extract from them. In this paper, we introduce the problem of finding a tensor of adaptive aggregated granularity that can be decomposed to reveal meaningful latent concepts (structures) from datasets that, in their original form, are not amenable to tensor analysis. Such datasets fall under the broad category of sparse point processes that evolve over space and/or time. To the best of our knowledge, this is the first work that explores adaptive granularity aggregation in tensors. Furthermore, we formally define the problem and discuss different definitions of "good structure" that are in practice and show that the optimal solution is of prohibitive combinatorial complexity. Subsequently, we propose an efficient and effective greedy algorithm called ICEBREAKER, which follows a number of intuitive decision criteria that locally maximize the "goodness of structure," resulting in high-quality tensors. We evaluate our method on synthetic, semi-synthetic, and real datasets. In all the cases, our proposed method constructs tensors that have a very high structure quality.

Surface-passivated Cu conductors for high-temperature sulfurous environments

Advanced materials capable of withstanding extreme environments garner extensive interest in the development of next-generation advanced anti-corrosion electronics. Herein, we report that the surface passivation of printed copper conductors imparts corrosion resistance in high-temperature sulfurous environments while maintaining a high electrical conductivity of 4.42 MS m^-1 when subjected to a sulfur-containing environment at 350 °C for 12 h. This study provides potential for the development of surface-passivated copper conductors that are resistant to the sulfidizing environments found in several applications of modern technology.

Saturable Absorption of Free-Electron Laser Radiation by Graphite near the Carbon K-Edge

The interaction of intense light with matter gives rise to competing nonlinear responses that can dynamically change material properties. Prominent examples are saturable absorption (SA) and two-photon absorption (TPA), which dynamically increase and decrease the transmission of a sample depending on pulse intensity, respectively. The availability of intense soft X-ray pulses from free-electron lasers (FELs) has led to observations of SA and TPA in separate experiments, leaving open questions about the possible interplay between and relative strength of the two phenomena. Here, we systematically study both phenomena in one experiment by exposing graphite films to soft X-ray FEL pulses of varying intensity. By applying real-time electronic structure calculations, we find that for lower intensities the nonlinear contribution to the absorption is dominated by SA attributed to ground-state depletion; our model suggests that TPA becomes more dominant for larger intensities (>10¹⁴ W/cm²). Our results demonstrate an approach of general utility for interpreting FEL spectroscopies.

External drivers of BOLD signal's non-stationarity

A fundamental challenge in neuroscience is to uncover the principles governing how the brain interacts with the external environment. However, assumptions about external stimuli fundamentally constrain current computational models. We show in silico that unknown external stimulation can produce error in the estimated linear time-invariant dynamical system. To address these limitations, we propose an approach to retrieve the external (unknown) input parameters and demonstrate that the estimated system parameters during external input quiescence uncover spatiotemporal profiles of external inputs over external stimulation periods more accurately. Finally, we unveil the expected (and unexpected) sensory and task-related extra-cortical input profiles using functional magnetic resonance imaging data acquired from 96 subjects (Human Connectome Project) during the resting-state and task scans. This dynamical systems model of the brain offers information on the structure and dimensionality of the BOLD signal's external drivers and shines a light on the likely external sources contributing to the BOLD signal's non-stationarity. Our findings show the role of exogenous inputs in the BOLD dynamics and highlight the importance of accounting for external inputs to unravel the brain's time-varying functional dynamics.

Tyrosine phosphatase activity is restricted by basic charge substituting mutation of substrates

Phosphorylation controls important cellular signals and its dysregulation leads to disease. While most phospho-regulation studies are focused on kinases, phosphatases are comparatively overlooked. Combining peptide arrays with SAMDI mass spectrometry, we show that tyrosine phosphatase activity is restricted by basic amino acids adjacent to phosphotyrosines. We validate this model using two β-catenin mutants associated with cancer (T653R/K) and a mouse model for intellectual disability (T653K). These mutants introduce a basic residue next to Y654, an established phosphorylation site where modification shifts β-catenin from cell-cell adhesions and towards its essential nuclear role as Wnt-signaling effector. We show that T653-basic mutant β-catenins are less efficiently dephosphorylated by phosphatases, leading to sustained Y654 phosphorylation and elevated Wnt signals, similar to those observed for Y654E phospho-mimic mutant mice. This model rationalizes how basic mutations proximal to phosphotyrosines can restrict counter-regulation by phosphatases, providing new mechanismistic and treatment insights for 6000+ potentially relevant cancer mutations.

A Critical Review of Communications in Multi-robot Systems

PURPOSE OF REVIEW

This review summarizes the broad roles that communication formats and technologies have played in enabling multi-robot systems. We approach this field from two perspectives: of robotic applications that need communication capabilities in order to accomplish tasks, and of networking technologies that have enabled newer and more advanced multi-robot systems.

RECENT FINDINGS

Through this review, we identify a dearth of work that holistically tackles the problem of co-design and co-optimization of robots and the networks they employ. We also highlight the role that data-driven and machine learning approaches play in evolving communication pipelines for multi-robot systems. In particular, we refer to recent work that diverges from hand-designed communication patterns, and also discuss the "sim-to-real" gap in this context.

SUMMARY

We present a critical view of the way robotic algorithms and their networking systems have evolved, and make the case for a more synergistic approach. Finally, we also identify four broad Open Problems for research and development, while offering a data-driven perspective for solving some of them.

Nanoarchitected metal/ceramic interpenetrating phase composites

Architected metals and ceramics with nanoscale cellular designs, e.g., nanolattices, are currently subject of extensive investigation. By harnessing extreme material size effects, nanolattices demonstrated classically inaccessible properties at low density, with exceptional potential for superior lightweight materials. This study expands the concept of nanoarchitecture to dense metal/ceramic composites, presenting co-continuous architectures of three-dimensional printed pyrolytic carbon shell reinforcements and electrodeposited nickel matrices. We demonstrate ductile compressive deformability with elongated ultrahigh strength plateaus, resulting in an extremely high combination of compressive strength and strain energy absorption. Simultaneously, property-to-weight ratios outperform those of lightweight nanolattices. Superior to cellular nanoarchitectures, interpenetrating nanocomposites may combine multiple size-dependent characteristics, whether mechanical or functional, which are radically antagonistic in existing materials. This provides a pathway toward previously unobtainable multifunctionality, extending far beyond lightweight structure applications.

Ultrafast Thermionic Electron Injection Effects on Exciton Formation Dynamics at a van der Waals Semiconductor/Metal Interface

Inorganic van der Waals bonded semiconductors such as transition metal dichalcogenides are the subject of intense research due to their electronic and optical properties which are promising for next-generation optoelectronic devices. In this context, understanding the carrier dynamics, as well as charge and energy transfer at the interface between metallic contacts and semiconductors, is crucial and yet quite unexplored. Here, we present an experimental study to measure the effect of mutual interaction between thermionically injected and directly excited carriers on the exciton formation dynamics in bulk WS₂. By employing a pump-push-probe scheme, where a pump pulse induces thermionic injection of electrons from a gold substrate into the conduction band of the semiconductor, and another delayed push pulse that excites direct transitions in the WS₂, we can isolate the two processes experimentally and thus correlate the mutual interaction with its effect on the ultrafast dynamics in WS₂. The fast decay time constants extracted from the experiments show a decrease with an increasing ratio between the injected and directly excited charge carriers, thus disclosing the impact of thermionic electron injection on the exciton formation dynamics. Our findings might offer a new vibrant direction for the integration of photonics and electronics, especially in active and photodetection devices, and, more in general, in upcoming all-optical nanotechnologies.

Molecular-Weight-Dependent Interplay of Brittle-to-Ductile Transition in High-Strain-Rate Cold Spray Deposition of Glassy Polymers

Based on the cold spray technique, the solvent-free and solid-state deposition of glassy polymers is envisioned. Adiabatic inelastic deformation mechanisms in the cold spray technique are studied through high-velocity collisions (<1000 m/s) of polystyrene microparticles against stationary target substrates of polystyrene and silicon. During extreme collisions, a brittle-to-ductile transition occurs, leading to either fracture- or shear-dominant inelastic deformation of the colliding microparticles. Due to the nonlinear interplay between the adiabatic shearing and the thermal softening of polystyrene, the plastic shear flow becomes the dominant deformation channel over brittle fragmentation when increasing the rigidity of the target substrate. High molecular weights (>20 kDa) are essential to hinder the evolution of brittle fracture and promote shear-induced heating beyond the glass transition temperature of polystyrene. However, an excessively high molecular weight (∼100 kDa) reduces the adhesion of the microparticles to the substrate due to insufficient wetting of the softened polystyrene. Due to the two competing viscoelastic effects, proper selection of molecular weight becomes critical for the cold spray technique of glassy polymers.

Using the DiCoT framework for integrated multimodal analysis in mixed-reality training environments

Simulation-based training (SBT) programs are commonly employed by organizations to train individuals and teams for effective workplace cognitive and psychomotor skills in a broad range of applications. Distributed cognition has become a popular cognitive framework for the design and evaluation of these SBT environments, with structured methodologies such as Distributed Cognition for Teamwork (DiCoT) used for analysis. However, the analysis and evaluations generated by such distributed cognition frameworks require extensive domain-knowledge and manual coding and interpretation, and the analysis is primarily qualitative. In this work, we propose and develop the application of multimodal learning analysis techniques to SBT scenarios. Using these analysis methods, we can use the rich multimodal data collected in SBT environments to generate more automated interpretations of trainee performance that supplement and extend traditional DiCoT analysis. To demonstrate the use of these methods, we present a case study of nurses training in a mixed-reality manikin-based (MRMB) training environment. We show how the combined analysis of the video, speech, and eye-tracking data collected as the nurses train in the MRMB environment supports and enhances traditional qualitative DiCoT analysis. By applying such quantitative data-driven analysis methods, we can better analyze trainee activities online in SBT and MRMB environments. With continued development, these analysis methods could be used to provide targeted feedback to learners, a detailed review of training performance to the instructors, and data-driven evidence for improving the environment to simulation designers.

Aqueous Electrolytes Reinforced by Mg and Ca Ions for Highly Reversible Fe Metal Batteries

Iron (Fe) metal batteries, such as Fe-ion batteries and all Fe flow batteries, are promising energy storage technologies for grid applications due to the extremely low cost of Fe and Fe salts. Nonetheless, the cycle life of Fe metal batteries is poor primarily due to the low Coulombic efficiency of the Fe deposition/stripping reaction. Current aqueous electrolytes based on Fe chloride or sulfate salts can only operate at a Coulombic efficiency of <91% under mild operation conditions (<5 mA/cm²), largely due to undesired hydrogen evolution reaction (HER). This work reports a series of novel Fe electrolytes, Fe electrolytes reinforced with Mg ions (FERMI) and Ca ions (FERCI), which have remarkably better Coulombic efficiency, higher conductivity, and faster deposition/stripping kinetics. By the addition of 4.5 M MgCl₂ or CaCl₂ into the baseline FeCl₂ electrolyte, the Fe deposition/stripping efficiency can be significantly improved to 99.1%, which greatly boosts the cycling performance of Fe metal batteries in both half-cells and full-cells. Mechanistic studies reveal that the remarkably improved efficiency is due to a reduced amount of "dead Fe" as well as suppressed HER. By the combination of experiments and molecular dynamics and density functional theory computation, the electrolyte structure is revealed, and the mechanism for enhanced water reduction resistance is elucidated. These novel electrolytes not only enable a highly reversible Fe metal anode for low-cost energy storage technologies but also have the potential to address the HER side reaction problem in other electrochemical technologies based on aqueous electrolytes, such as CO₂ reduction, NH₃ synthesis, etc.

Pump Up the Jam: Granular Media as a Quasi-Hydraulic Fluid for Independent Control Over Isometric and Isotonic Actuation

Elastomer-granule composites have been used to switch between soft and stiff states by applying negative pressure differentials that cause the membrane to squeeze the internal grains, inducing dilation and jamming. Applications of this phenomenon have ranged from universal gripping to adaptive mobility. Previously, the combination of this jamming phenomenon with the ability to transport grains across multiple soft actuators for shape morphing has not yet been demonstrated. In this paper, the authors demonstrate the use of hollow glass spheres as granular media that functions as a jammable "quasi-hydraulic" fluid in a fluidic elastomeric actuator that better mimics a key featur of animal musculature: independent control over i) isotonic actuation for motion; and ii) isometric actuation for stiffening without shape change. To best implement the quasi-hydraulic fluid, the authors design and build a fluidic device. Leveraging this combination of physical properties creates a new option for fluidic actuation that allows higher specific stiffness actuators using lower volumetric flow rates in addition to independent control over shape and stiffness. These features are showcased in a robotic catcher's mitt by stiffening the fluid in the glove's open configuration for catching, unjamming the media, then pumping additional fluid to the mitt to inflate and grasp.

Building FAIR Functionality: Annotating Events in Time Series Data Using Hierarchical Event Descriptors (HED)

Human electrophysiological and related time series data are often acquired in complex, event-rich environments. However, the resulting recorded brain or other dynamics are often interpreted in relation to more sparsely recorded or subsequently-noted events. Currently a substantial gap exists between the level of event description required by current digital data archiving standards and the level of annotation required for successful analysis of event-related data across studies, environments, and laboratories. Manifold challenges must be addressed, most prominently ontological clarity, vocabulary extensibility, annotation tool availability, and overall usability, to allow and promote sharing of data with an effective level of descriptive detail for labeled events. Motivating data authors to perform the work needed to adequately annotate their data is a key challenge. This paper describes new developments in the Hierarchical Event Descriptor (HED) system for addressing these issues. We recap the evolution of HED and its acceptance by the Brain Imaging Data Structure (BIDS) movement, describe the recent release of HED-3G, a third generation HED tools and design framework, and discuss directions for future development. Given consistent, sufficiently detailed, tool-enabled, field-relevant annotation of the nature of recorded events, prospects are bright for large-scale analysis and modeling of aggregated time series data, both in behavioral and brain imaging sciences and beyond.

Multi-parametric functional imaging of cell cultures and tissues with a CMOS microelectrode array

Electrode-based impedance and electrochemical measurements can provide cell-biology information that is difficult to obtain using optical-microscopy techniques. Such electrical methods are non-invasive, label-free, and continuous, eliminating the need for fluorescence reporters and overcoming optical imaging's throughput/temporal resolution limitations. Nonetheless, electrode-based techniques have not been heavily employed because devices typically contain few electrodes per well, resulting in noisy aggregate readouts. Complementary metal-oxide-semiconductor (CMOS) microelectrode arrays (MEAs) have sometimes been used for electrophysiological measurements with thousands of electrodes per well at sub-cellular pitches, but only basic impedance mappings of cell attachment have been performed outside of electrophysiology. Here, we report on new field-based impedance mapping and electrochemical mapping/patterning techniques to expand CMOS-MEA cell-biology applications. The methods enable accurate measurement of cell attachment, growth/wound healing, cell-cell adhesion, metabolic state, and redox properties with single-cell spatial resolution (20 μm electrode pitch). These measurements allow the quantification of adhesion and metabolic differences of cells expressing oncogenes versus wild-type controls. The multi-parametric, cell-population statistics captured by the chip-scale integrated device opens up new avenues for fully electronic high-throughput live-cell assays for phenotypic screening and drug discovery applications.

Modeling human migration driven by changing mindset, agglomeration, social ties, and the environment

Migration is an adaptation strategy to unfavorable conditions and is governed by a complex set of socio-economic and environmental drivers. Here we identified important drivers relatively underrepresented in many migration models-CHanging mindset, Agglomeration, Social ties, and the Environment (CHASE)-and asked: How does the interplay between these drivers influence transient dynamics and long-term outcomes of migration? We addressed this question by developing and analyzing a parsimonious Markov chain model. Our findings suggest that these drivers interact in nonlinear and complex ways. The system exhibits legacy effects, highlighting the importance of including migrants' changing priorities. The increased characteristic population size of the system counter-intuitively leads to fewer surviving cities, and this effect is mediated by how fast migrants change their mindsets and how strong the social ties are. Strong social ties result in less diverse populations across cities, but this effect is influenced by how many cities remain. To our knowledge, this is the first time that these drivers are incorporated in one coherent, mechanistic, parsimonious model and the effects of their interplay on migration systematically studied. The complex interplay underscores the need to incorporate these drivers into mechanistic migration models and implement such models for real-world cases.

Shortcuts to Thermodynamic Computing: The Cost of Fast and Faithful Information Processing

Landauer's Principle states that the energy cost of information processing must exceed the product of the temperature, Boltzmann's constant, and the change in Shannon entropy of the information-bearing degrees of freedom. However, this lower bound is achievable only for quasistatic, near-equilibrium computations-that is, only over infinite time. In practice, information processing takes place in finite time, resulting in dissipation and potentially unreliable logical outcomes. For overdamped Langevin dynamics, we show that counterdiabatic potentials can be crafted to guide systems rapidly and accurately along desired computational paths, providing shortcuts that allow for the precise design of finite-time computations. Such shortcuts require additional work, beyond Landauer's bound, that is irretrievably dissipated into the environment. We show that this dissipated work is proportional to the computation rate as well as the square of the information-storing system's length scale. As a paradigmatic example, we design shortcuts to create, erase, and transfer a bit of information metastably stored in a double-well potential. Though dissipated work generally increases with operation fidelity, we show that it is possible to compute with perfect fidelity in finite time with finite work. We also show that the robustness of information storage affects an operation's energetic cost-specifically, the dissipated work scales as the information lifetime of the bistable system. Our analysis exposes a rich and nuanced relationship between work, speed, size of the information-bearing degrees of freedom, storage robustness, and the difference between initial and final informational statistics.

Impact of Honey Bee Migratory Management on Pathogen Loads and Immune Gene Expression is Affected by Complex Interactions With Environment, Worker Life History, and Season

The effects of honey bee management, such as intensive migratory beekeeping, are part of the ongoing debate concerning causes of colony health problems. Even though comparisons of disease and pathogen loads among differently managed colonies indicate some effects, the direct impact of migratory practices on honey bee pathogens is poorly understood. To test long- and short-term impacts of managed migration on pathogen loads and immunity, experimental honey bee colonies were maintained with or without migratory movement. Individuals that experienced migration as juveniles (e.g., larval and pupal development), as adults, or both were compared to control colonies that remained stationary and therefore did not experience migratory relocation. Samples at different ages and life-history stages (hive bees or foragers), taken at the beginning and end of the active season, were analyzed for pathogen loads and physiological markers of health. Bees exposed to migratory management during adulthood had increased levels of the AKI virus complex (Acute bee paralysis, Kashmir bee, and Israeli acute bee paralysis viruses) and decreased levels of antiviral gene expression (dicer-like). However, those in stationary management as adults had elevated gut parasites (i.e. trypanosomes). Effects of environment during juvenile development were more complex and interacted with life-history stage and season. Age at collection, life-history stage, and season all influenced numerous factors from viral load to immune gene expression. Although the factors that we examined are not independent, the results illuminate potential factors in both migratory and nonmigratory beekeeping that are likely to contribute to colony stress, and also indicate potential mitigation measures.

Understanding Fragility and Engineering Activation Stability in Two-Dimensional Covalent Organic Frameworks

The sensitivity of covalent organic frameworks (COFs) to pore collapse during activation processes is generally termed activation stability, and activation stability is important for achieving and maintaining COF crystallinity and porosity which are relevant to a variety of applications. However, current understanding of COF stability during activation is insufficient, and prior studies have focused primarily on thermal stability or on the activation stability of other porous materials, such as metal–organic frameworks (MOFs). In this work, we demonstrate and implement a versatile experimental approach to quantify activation stability of COFs and use this to establish a number of relationships between their pore size, the type of pore substituents, pore architecture, and structural robustness. Additionally, density functional theory calculations reveal the impact on both inter-and intra-layer interactions, which govern activation stability, and we demonstrate that activation stability can be systematically tuned using a multivariate synthesis approach involving mixtures of functionalized and unfunctionalized COF building blocks. Our findings provide novel fundamental insights into the activation stability of COFs and offer guidance for the design of more robust COFs.

We establish relationships between COF pore size, the type of pore substituent, pore architecture, and structural robustness and demonstrate that activation stability can be systematically tuned using a multivariate synthesis approach.

Combination treatment optimization using a pan-cancer pathway model

The design of efficient combination therapies is a difficult key challenge in the treatment of complex diseases such as cancers. The large heterogeneity of cancers and the large number of available drugs renders exhaustive in vivo or even in vitro investigation of possible treatments impractical. In recent years, sophisticated mechanistic, ordinary differential equation-based pathways models that can predict treatment responses at a molecular level have been developed. However, surprisingly little effort has been put into leveraging these models to find novel therapies. In this paper we use for the first time, to our knowledge, a large-scale state-of-the-art pan-cancer signaling pathway model to identify candidates for novel combination therapies to treat individual cancer cell lines from various tissues (e.g., minimizing proliferation while keeping dosage low to avoid adverse side effects) and populations of heterogeneous cancer cell lines (e.g., minimizing the maximum or average proliferation across the cell lines while keeping dosage low). We also show how our method can be used to optimize the drug combinations used in sequential treatment plans—that is, optimized sequences of potentially different drug combinations—providing additional benefits. In order to solve the treatment optimization problems, we combine the Covariance Matrix Adaptation Evolution Strategy (CMA-ES) algorithm with a significantly more scalable sampling scheme for truncated Gaussian distributions, based on a Hamiltonian Monte-Carlo method. These optimization techniques are independent of the signaling pathway model, and can thus be adapted to find treatment candidates for other complex diseases than cancers as well, as long as a suitable predictive model is available.

Combination therapies are a promising approach to counter complex diseases such as cancers. Two key difficulties in the design of effective cancer combination therapies are the large number of available drugs and the heterogeneity of cancers which render exhaustive laboratory studies impractical. In recent years, sophisticated signaling pathway models that can predict responses to combination treatments at a molecular level have been developed. This motivates the question of how one can leverage mechanistic models to identify candidates for novel combination treatments. In this paper we propose a combination treatment optimization framework which employs a large-scale pan-cancer pathway model. We formulate treatment optimization problems for single cell lines and heterogeneous populations of cancer cells. We further investigate sequential treatment plans and combine an existing evolutionary algorithm with an efficient Hamiltonian Monte-Carlo based sampling scheme. During extensive simulation studies our approach identified combination therapies which are predicted to be more effective than conventional treatments. We hope that one day in silico experiments will be used to identify a small set of promising treatment candidates which can then form a starting point for laboratory studies, allowing for an efficient use of limited resources and accelerated discovery of effective therapies.

Porphyrin-based donor-acceptor COFs as efficient and reusable photocatalysts for PET-RAFT polymerization under broad spectrum excitation

Covalent organic frameworks (COFs) are crystalline and porous organic materials attractive for photocatalysis applications due to their structural versatility and tunable optical and electronic properties. The use of photocatalysts (PCs) for polymerizations enables the preparation of well-defined polymeric materials under mild reaction conditions. Herein, we report two porphyrin-based donor-acceptor COFs that are effective heterogeneous PCs for photoinduced electron transfer-reversible addition-fragmentation chain transfer (PET-RAFT). Using density functional theory (DFT) calculations, we designed porphyrin COFs with strong donor-acceptor characteristics and delocalized conduction bands. The COFs were effective PCs for PET-RAFT, successfully polymerizing a variety of monomers in both organic and aqueous media using visible light (λ max from 460 to 635 nm) to produce polymers with tunable molecular weights (MWs), low molecular weight dispersity, and good chain-end fidelity. The heterogeneous COF PCs could also be reused for PET-RAFT polymerization at least 5 times without losing photocatalytic performance. This work demonstrates porphyrin-based COFs that are effective catalysts for photo-RDRP and establishes design principles for the development of highly active COF PCs for a variety of applications.

Cation- and pH-Dependent Hydrogen Evolution and Oxidation Reaction Kinetics

The production of molecular hydrogen by catalyzing water splitting is central to achieving the decarbonization of sustainable fuels and chemical transformations. In this work, a series of structure-making/breaking cations in the electrolyte were investigated as spectator cations in hydrogen evolution and oxidation reactions (HER/HOR) in the pH range of 1 to 14, whose kinetics was found to be altered by up to 2 orders of magnitude by these cations. The exchange current density of HER/HOR was shown to increase with greater structure-making tendency of cations in the order of Cs⁺ < Rb⁺ < K⁺ < Na⁺ < Li⁺, which was accompanied by decreasing reorganization energy from the Marcus-Hush-Chidsey formalism and increasing reaction entropy. Invoking the Born model of reorganization energy and reaction entropy, the static dielectric constant of the electrolyte at the electrified interface was found to be significantly lower than that of bulk, decreasing with the structure-making tendency of cations at the negatively charged Pt surface. The physical origin of cation-dependent HER/HOR kinetics can be rationalized by an increase in concentration of cations on the negatively charged Pt surface, altering the interfacial water structure and the H-bonding network, which is supported by classical molecular dynamics simulation and surface-enhanced infrared absorption spectroscopy. This work highlights immense opportunities to control the reaction rates by tuning interfacial structures of cation and solvents.

Solution-shearing of dielectric polymer with high thermal conductivity and electric insulation

Polymer dielectrics, an insulating material ubiquitous in electrical power systems, must be ultralight, mechanically and dielectrically strong, and very thermally conductive. However, electric and thermal transport parameters are intercorrelated in a way that works against the occurrence of thermally conductive polymer electric insulators. Here, we describe how solution gel-shearing–strained polyethylene yields an electric insulating material with an outstanding in-plane thermal conductivity of 10.74 W m⁻¹ K⁻¹ and an average dielectric constant of 4.1. The dielectric constant and loss of such sheared polymer electric insulators are nearly independent of the frequency and a wide temperature range. The gel-shearing aligns ultrahigh–molecular weight polymer crystalline chains for the formation of separated and aligned nanoscale fibrous arrays. Together with lattice strains and the presence of boron nitride nanosheets, the dielectric polymer shows high current density carrying and high operating temperature, which is attributed to greatly enhanced heat conduction.

A Review of THz Technologies for Rapid Sensing and Detection of Viruses including SARS-CoV-2

Virus epidemics such as Ebola virus, Zika virus, MERS-coronavirus, and others have wreaked havoc on humanity in the last decade. In addition, a coronavirus (SARS-CoV-2) pandemic and its continuously evolving mutants have become so deadly that they have forced the entire technical advancement of healthcare into peril. Traditional ways of detecting these viruses have been successful to some extent, but they are costly, time-consuming, and require specialized human resources. Terahertz-based biosensors have the potential to lead the way for low-cost, non-invasive, and rapid virus detection. This review explores the latest progresses in terahertz technology-based biosensors for the virus, viral particle, and antigen detection, as well as upcoming research directions in the field.