Rapid In Situ Thermal Decontamination of Wearable Composite Textile Materials.

Pandemics stress supply lines and generate shortages of personal protective equipment (PPE), in part because most PPE is single-use and disposable, resulting in a need for constant replenishment to cope with high-volume usage. To better prepare for the next pandemic and to reduce waste associated with disposable PPE, we present a composite textile material capable of thermally decontaminating its surface via Joule heating. This material can achieve high surface temperatures (>100 °C) and inactivate viruses quickly (<5 s of heating), as evidenced experimentally with the surrogate virus HCoV-OC43 and in agreement with analytical modeling for both HCoV-OC43 and SARS-CoV-2. Furthermore, it does not require doffing because it remains relatively cool near the skin (<40 °C). The material can be easily integrated into clothing and provides a rapid, reusable, in situ decontamination method capable of reducing PPE waste and mitigating the risk of supply line disruptions in times of need.

Mitigating Contamination with Nanostructure-Enabled Ultraclean Storage.

Airborne hydrocarbon contamination hinders nanomanufacturing, limits characterization techniques, and generates controversies regarding fundamental studies of advanced materials; consequently, we urgently need effective and scalable clean storage techniques. In this work, we propose an approach to clean storage using an ultraclean nanotextured storage medium as a getter. Experiments show that our proposed approach can maintain surface cleanliness for more than 1 week and can even passively clean initially contaminated samples during storage. We theoretically analyzed the contaminant adsorption-desorption process with different values of storage medium surface roughness, and our model predictions showed good agreement with experiments for smooth, nanotextured, and hierarchically textured surfaces, providing guidelines for the design of future clean storage systems. The proposed strategy offers a promising approach for portable and cost-effective storage systems that minimize hydrocarbon contamination in applications requiring clean surfaces, including nanofabrication, device storage and transportation, and advanced metrology.

A wearable textile-based pneumatic energy harvesting system for assistive robotics.

Wearable assistive, rehabilitative, and augmentative devices currently require bulky power supplies, often making these tools more of a burden than an asset. This work introduces a soft, low-profile, textile-based pneumatic energy harvesting system that extracts power directly from the foot strike of a user during walking. Energy is harvested with a textile pump integrated into the insole of the user's shoe and stored in a wearable textile bladder to operate pneumatic actuators on demand, with system performance optimized based on a mechano-fluidic model. The system recovered a maximum average power of nearly 3 W with over 20% conversion efficiency-outperforming electromagnetic, piezoelectric, and triboelectric alternatives-and was used to power a wearable arm-lift device that assists shoulder motion and a supernumerary robotic arm, demonstrating its capability as a lightweight, low-cost, and comfortable solution to support adults with upper body functional limitations in activities of daily living.

Logic-enabled textiles.

Textiles hold great promise as a soft yet durable material for building comfortable robotic wearables and assistive devices at low cost. Nevertheless, the development of smart wearables composed entirely of textiles has been hindered by the lack of a viable sheet-based logic architecture that can be implemented using conventional fabric materials and textile manufacturing processes. Here, we develop a fully textile platform for embedding pneumatic digital logic in wearable devices. Our logic-enabled textiles support combinational and sequential logic functions, onboard memory storage, user interaction, and direct interfacing with pneumatic actuators. In addition, they are designed to be lightweight, easily integrable into regular clothing, made using scalable fabrication techniques, and durable enough to withstand everyday use. We demonstrate a textile computer capable of input-driven digital logic for controlling untethered wearable robots that assist users with functional limitations. Our logic platform will facilitate the emergence of future wearables powered by embedded fluidic logic that fully leverage the innate advantages of their textile construction.

Necrobotics: Biotic Materials as Ready-to-Use Actuators.

Designs perfected through evolution have informed bioinspired animal-like robots that mimic the locomotion of cheetahs and the compliance of jellyfish; biohybrid robots go a step further by incorporating living materials directly into engineered systems. Bioinspiration and biohybridization have led to new, exciting research, but humans have relied on biotic materials-non-living materials derived from living organisms-since their early ancestors wore animal hides as clothing and used bones for tools. In this work, an inanimate spider is repurposed as a ready-to-use actuator requiring only a single facile fabrication step, initiating the area of "necrobotics" in which biotic materials are used as robotic components. The unique walking mechanism of spiders-relying on hydraulic pressure rather than antagonistic muscle pairs to extend their legs-results in a necrobotic gripper that naturally resides in its closed state and can be opened by applying pressure. The necrobotic gripper is capable of grasping objects with irregular geometries and up to 130% of its own mass. Furthermore, the gripper can serve as a handheld device and innately camouflages in outdoor environments. Necrobotics can be further extended to incorporate biotic materials derived from other creatures with similar hydraulic mechanisms for locomotion and articulation.

Efficacy and self-similarity of SARS-CoV-2 thermal decontamination.

Dry heat decontamination has been shown to effectively inactivate viruses without compromising the integrity of delicate personal protective equipment (PPE), allowing safe reuse and helping to alleviate shortages of PPE that have arisen due to COVID-19. Unfortunately, current thermal decontamination guidelines rely on empirical data which are often sparse, limited to a specific virus, and unable to provide fundamental insight into the underlying inactivation reaction. In this work, we experimentally quantified dry heat decontamination of SARS-CoV-2 on disposable masks and validated a model that treats the inactivation reaction as thermal degradation of macromolecules. Furthermore, upon nondimensionalization, all of the experimental data collapse onto a unified curve, revealing that the thermally driven decontamination process exhibits self-similar behavior. Our results show that heating surgical masks to 70 °C for 5 min inactivates over 99.9% of SARS-CoV-2. We also characterized the chemical and physical properties of disposable masks after heat treatment and did not observe degradation. The model presented in this work enables extrapolation of results beyond specific temperatures to provide guidelines for safe PPE decontamination. The modeling framework and self-similar behavior are expected to extend to most viruses-including yet-unencountered novel viruses-while accounting for a range of environmental conditions.

Effect of daily temperature fluctuations on virus lifetime.

Epidemiological studies based on statistical methods indicate inverse correlations between virus lifetime and both (i) daily mean temperature and (ii) diurnal temperature range (DTR). While thermodynamic models have been used to predict the effect of constant-temperature surroundings on virus inactivation rate, the relationship between virus lifetime and DTR has not been explained using first principles. Here, we model the inactivation of viruses based on temperature-dependent chemical kinetics with a time-varying temperature profile to account for the daily mean temperature and DTR simultaneously. The exponential Arrhenius relationship governing the rate of virus inactivation causes fluctuations above the daily mean temperature during daytime to increase the instantaneous rate of inactivation by a much greater magnitude than the corresponding decrease in inactivation rate during nighttime. This asymmetric behavior results in shorter predicted virus lifetimes when considering DTR and consequently reveals a potential physical mechanism for the inverse correlation observed between the number of cases and DTR reported in statistical epidemiological studies. In light of the ongoing COVID-19 pandemic, a case study on the effect of daily mean temperature and DTR on the lifetime of SARS-CoV-2 was performed for the five most populous cities in the United States. In Los Angeles, where mean monthly temperature fluctuations are low (DTR ≈ 7 °C), accounting for DTR decreases predicted SARS-CoV-2 lifetimes by only 10%; conversely, accounting for DTR for a similar mean temperature but larger mean monthly temperature fluctuations in Phoenix (DTR ≈ 15 °C) decreases predicted lifetimes by 50%. The modeling framework presented here provides insight into the independent effects of mean temperature and DTR on virus lifetime, and a significant impact on transmission rate is expected, especially for viruses that pose a high risk of fomite-mediated transmission.

A predictive model of the temperature-dependent inactivation of coronaviruses.

The COVID-19 pandemic has stressed healthcare systems and supply lines, forcing medical doctors to risk infection by decontaminating and reusing single-use personal protective equipment. The uncertain future of the pandemic is compounded by limited data on the ability of the responsible virus, SARS-CoV-2, to survive across various climates, preventing epidemiologists from accurately modeling its spread. However, a detailed thermodynamic analysis of experimental data on the inactivation of SARS-CoV-2 and related coronaviruses can enable a fundamental understanding of their thermal degradation that will help model the COVID-19 pandemic and mitigate future outbreaks. This work introduces a thermodynamic model that synthesizes existing data into an analytical framework built on first principles, including the rate law for a first-order reaction and the Arrhenius equation, to accurately predict the temperature-dependent inactivation of coronaviruses. The model provides much-needed thermal decontamination guidelines for personal protective equipment, including masks. For example, at 70 °C, a 3-log (99.9%) reduction in virus concentration can be achieved, on average, in 3 min (under the same conditions, a more conservative decontamination time of 39 min represents the upper limit of a 95% interval) and can be performed in most home ovens without reducing the efficacy of typical N95 masks as shown in recent experimental reports. This model will also allow for epidemiologists to incorporate the lifetime of SARS-CoV-2 as a continuous function of environmental temperature into models forecasting the spread of the pandemic across different climates and seasons.