Small Devices Research Articles

Phytic acid-based super antifreeze multifunctional conductive hydrogel for human motion monitoring and energy harvesting devices

Conductive hydrogels have broad application prospects in the field of self-powered sensors and energy harvesting devices due to their good electrical conductivity and flexibility. However, at low temperatures, conductive hydrogels are usually frozen, resulting in problems such as poor electrical conductivity and flexibility, which seriously hinder the application of these fields. To address these challenges, phytic acid (PA) was employed as the crosslinker and antifreezing agent in this study. Polyvinyl alcohol (PVA), 2-acrylamido-2-methyl-1-propanesulfonic acid (AMPS), chitosan (CS), and PA were used as raw materials to successfully synthesize PVA/AMPS/CS/PA (PACP) multifunctional conductive hydrogels with outstanding antifreeze and water retention properties (freezing point below −80 °C, 30-day water loss rate of 3.5%). In addition, PACP hydrogels exhibit exceptional electrical conductivity of up to 9.4 S/m, along with antibacterial and biocompatible properties, enabling their utilization as wearable sensors on human skin tissue. Notably, the PACP hydrogel-based triboelectric nanogenerator (PACP-TENG) excels in accurately monitoring human motion and powering small electronic devices, facilitating remote control of small light switches. The resulting open circuit voltage is as high as 314 V at 25 °C and about 110 V at low temperature. Therefore, PACP hydrogels with excellent properties are expected to expand their applications in these fields.

Asymmetric fluid flow in helical pipes inspired by shark intestines

Unlike human intestines, which are long, hollow tubes, the intestines of sharks and rays contain interior helical structures surrounding a cylindrical hole. One function of these structures may be to create asymmetric flow, favoring passage of fluid down the digestive tract, from anterior to posterior. Here, we design and 3D print biomimetic models of shark intestines, in both rigid and deformable materials. We use the rigid models to test which physical parameters of the interior helices (the pitch, the hole radius, the tilt angle, and the number of turns) yield the largest flow asymmetries. These asymmetries exceed those of traditional Tesla valves, structures specifically designed to create flow asymmetry without any moving parts. When we print the biomimetic models in elastomeric materials so that flow can couple to the structure's shape, flow asymmetry is significantly amplified; it is sevenfold larger in deformable structures than in rigid structures. Last, we 3D-print deformable versions of the intestine of a dogfish shark, based on a tomogram of a biological sample. This biomimic produces flow asymmetry comparable to traditional Tesla valves. The ability to influence the direction of a flow through a structure has applications in biological tissues and artificial devices across many scales, from large industrial pipelines to small microfluidic devices.

Advances and Challenges in Large-Area Perovskite Light-Emitting Diodes.

Metal halide perovskite light-emitting diodes (PeLEDs) have shown promise for high-definition displays and flat-panel lighting because of their wide color gamut, narrow emission band, and high brightness. The external quantum efficiency of PeLEDs increased rapidly from ≈1% to more than 25% in the past few years. However, most of these high-performance devices are fabricated using a spin coating method with a small device area of <0.1 cm2, limiting their commercial applications. Recently, large-area PeLEDs have attracted growing attention and significant breakthroughs have been reported. This perspective first introduces the pros and cons of each technique in making large-area PeLEDs. The advances in the fabrication of large-area PeLEDs are then summarized using spin coating and mass-production methods such as inkjet printing, blade coating, and thermal evaporation. Moreover, the challenging issues will be discussed that are urgent to be solved for large-area PeLEDs.

A super convenient and specific CRISPR/Cas12a diagnostic platform for toxigenic Burkholderia gladioli based on virulence genes

In recent years, food poisoning cases caused by toxigenic Burkholderia gladioli pv. cocovenenans (BGC) have been frequently reported in Asian countries like China and Indonesia, which seriously threaten food safety and public health. BGC is the only subspecies within the Burkholderia gladioli species that can cause food poisoning in humans, but there is a high degree of sequence homology between the different subspecies, making detection and differentiation difficult. In this study, a specific DNA target was screened out from the bongkrekic acid biosynthentic gene cluster. A rapid, sensitive, and highly specific CRISPR/Cas12a molecular diagnostic platform was developed for the first time to differentiate between toxigenic and non-toxigenic subspecies. Genomic DNA was extracted from the samples after a simple boiling process. The sensitivity was evaluated from three different levels, the genomic DNA sensitivity was determined to be 10−4 ng/μL, the target DNA sensitivity was determined to be 14.9 copies/μL, and the bacterial suspension sensitivity was confirmed to be 1.52 CFU/mL. The detection limit of BGC in artificially contaminated coconut water samples was determined to be 1.50 CFU/mL without any microbial enrichment. Including sample pretreatment and DNA extraction process, the whole detection of BGC in samples can be completed within 60 min under the assistance of some small and portable devices. This super convenient diagnostic platform is suitable for food field testing and provides a theoretical basis for the specific identification of BGC.

Secured Data Transmission in Network using Nano Technology

A Nanonetwork is mutually joined or related nanomachines used in communication system and helps to estimate uncomplicated tasks, making them simple to understand which are able to execute a very easy and uncomplicated tasks and is therefore easy to understand, such as computing, data storing, sensing. Nanonetworks consists of range of operation by permitting them to coordinate, share and combine information. Nanonetworks enables new application of Nanotechnology in biomedical field. The foremost concept behind nanotechnology and nanoscience was to control and manipulate atoms and molecules. The term “nanotechnology” was coined by professor Norio Taniguchi over a decade later. A Nanotechnology was in a position to develop small devices small . A Nanonetwork comprises of nanoscale computing devices and sensors that helps in communication at the nanoscale. Applications in networking consists of wireless technology, IOT, wireless and mobile nano devices. Based on the application requirement nanonetworks classifies the nanomachines to various application requirements like nano transmitters, Nano receiver, Nano Robot, Nano controller. Communication security is taken care from unauthorized access. Security composes of physical security, emission security, encryption security, transmission security. Some of the best tools used for secure business communication are perception point, Mail fence, rocket chat.

Magnetically frequency-modulated piezoelectric energy harvester for ultra-low-frequency wave motion

In this paper, a magnetically frequency modulated piezoelectric energy harvester (MFPEH) for ultra-low-frequency wave motion is proposed. The MFPEH employs multiple sets of magnets coupled to achieve optimal electrical output at ultra-low-frequency excitation (&lt;2 Hz). A straight rack and pinion drive converts the vertical motion of the float into rotational motion, and magnets on the rotor provide excitation to the piezoelectric cantilever. As a result, the MFPEH converts random wave motion into steady rotational motion. The MFPEH uses a rational combination of multiple sets of magnets and frequency modulation by magnetic coupling, which greatly improves the output performance. This paper describes the key factors affecting the output performance of MFPEH. An experimental system simulating a wave environment is designed and constructed. Under the most effective experimental parameters, the MFPEH achieves an optimal power of 32.76 mW. In a real wave environment, the MFPEH was able to illuminate 85 LEDs and power a thermohygrometer for normal operation. These results validate the potential of the MFPEH to power small electronic devices.

Thermoelectric generators versus photovoltaic solar panels: Power and cost analysis

In the current study, the concept of building a power plant using thermoelectric generator (TEG) modules is investigated, both technically and economically. The hypothesized thermoelectric generation power plant is a modular system, consisting of a large array of electrically connected thermoelectric generator units for generating clean electricity with-out greenhouse gas (GHG) emissions, noise, or hazardous solid wastes. The envisioned thermoelectric generation power plant (TEGPP) considered here is assumed to utilize solar radiation as a heat source, and water as a heat sink. The viability of such a concept is examined in the current study based on available specifications of a high-output thermo-electric generator module released in the market (TEG1-24111-6.0). Benchmarking is car-ried out considering a high-efficiency photovoltaic (PV) panel in the market (SunPower SPR-MAX3-400), assuming that it operates under standard solar radiation of 1,000 W, and with a standard panel temperature of 25 C, causing it to give an output electric power of 400 W (DC or direct current). It is found that in order to have an electric power of a thermoelectric generator unit similar to that of a photovoltaic panel of equal surface area, the temperature at the hot side of the thermoelectric generator unit should be about 70 C if the cold-side temperature is 30 C. However; under this output power equiva-lence, the price of the thermoelectric generator unit is about 90 times that of a photovol-taic panel of equal size (based on prices of October 2023). At an elevated hot-side tem-perature of 300 C for the thermoelectric generator unit (with the cold-side temperature being still 30 C), the thermoelectric generator unit can generate electric power that is about 25 times the power generated by a photovoltaic panel of an equal geometric area. This big boost in the output power still does not counteract the large cost difference be-tween the thermoelectric generator technology and the photovoltaic technology, where the per-watt(electric) cost in the case of thermoelectric generators is 3.5 times its value in the case of photovoltaic panels. Thus, the TEG technology in its intensified generation mode is still relatively more expensive compared to the PV technology. The practical im-plications of the current study are excluding the thermoelectric generation (based on the Seebeck effect) concept from large-scale commercial power plants, and viewing thermoe-lectric generators as sources of small electric power for waste heat management or con-venient power sources for small mobile devices.

UAV-IRS-assisted energy harvesting for edge computing based on deep reinforcement learning

In the internet of everything (IoE) era, the proliferation of internet of things (IoT) devices is accelerating rapidly. Particularly, smaller devices are increasingly constrained by hardware limitations that impact their computational capacity, communication bandwidth, and battery longevity. Our research explores a multi-device, multi-access edge computing (MEC) environment within small cells to address the challenges posed by the hardware limitations of IoT devices in this environment. We employ wireless power transfer (WPT) to ensure these IoT devices have sufficient energy for task processing. We propose a system architecture in which an intelligent reflective surface (IRS) is carried by an unmanned aerial vehicle (UAV) to enhance communication conditions. For sustainable energy harvesting (EH), we integrate a normal distribution into the objective function. We utilize a softmax deep double deterministic policy gradients (SD3) algorithm, based on deep reinforcement learning (DRL), to optimize the computational and communication capabilities of IoT devices. Simulation experiments demonstrate that our SD3-based EH edge computing (EHEC-SD3) algorithm surpasses existing DRL algorithms in our explored environments, achieving more than 90% in overall optimization and EH performance.

Super-strain, high sensitivity, and multi-responsive flexible sensor based on nanocellulose hydrogels

The application of flexible sensors based on conductive hydrogels in human motion monitoring has been widely studied. Here, a facile one-pot method is proposed to prepare nanocellulose/polyacrylamide/sodium alginate hydrogel (CNWs/SA/PAM). The resulting CNWs/SA/PAM hydrogel has exceptional mechanical properties (7850 %, 1.3 MPa) and high sensitivity (14200 %, GF=3.6, 68 ms). The nanocellulose crystals (CNWs) are bent and entangled with the backbone of the polyacrylamide/sodium alginate (PAM/SA) hydrogel network, effectively transferring external mechanical forces to the entire physical and chemical cross-linking domains. This feature considerably improves the tensile strength, strain sensing range, and sensitivity of the hydrogel. The CNWs/SA/PAM hydrogel exhibits a sensitive response to water molecules and temperature-stimulated shape memory behaviour and can act as a programmable deformation actuator. The hydrogel sensor can detect and monitor subtle human motion signals in a timely and accurate manner. It is designed as a single-electrode friction nanogenerator insole (insole-TENG) that can monitor the large motion states of the human body in real time and can be efficiently used for small self-powered electronic devices. This study will shed some light on developing cellulose hydrogels in multifunctional human motion health detection sensors, soft-actuated robots, and self-powered applications for small electronic devices.

Overdose Detection Among High-Risk Opioid Users Via a Wearable Chest Sensor in a Supervised Injecting Facility: Protocol for an Observational Study.

Opioid overdose is a global health crisis, affecting over 27 million individuals worldwide, with more than 100,000 drug overdose deaths in the United States in 2022-2023. This protocol outlines the development of the PneumoWave chest biosensor, a wearable device being designed to detect respiratory depression in real time through chest motion measurement, intending to enhance early intervention and thereby reduce fatalities. The study aims to (1) differentiate opioid-induced respiratory depression (OIRD) from nonfatal opioid use patterns to develop and refine an overdose detection algorithm and (2) examine participants' acceptability of the chest biosensor. The study adopts an observational design over a 6-month period. The biosensor, a small device, will be worn by consenting participants during injecting events to capture chest motion data. Safe injecting facilities (SIF) in Melbourne, Victoria (site 1), and Sydney, New South Wales (site 2), which are legally sanctioned spaces where individuals can use preobtained illicit drugs under medical supervision. Each site is anticipated to recruit up to 100 participants who inject opioids and attend the SIF. Participants will wear the biosensor during supervised injecting events at both sites. The biosensor will attempt to capture data on an anticipated 40 adverse drug events. The biosensor's ability to detect OIRD will be compared to the staff-identified events that use standard protocols for managing overdoses. Measurements will include (1) chest wall movement measured by the biosensor, securely streamed to a cloud, and analyzed to refine an overdose detection algorithm and (2) acute events or potential overdose identified by site staff. Acceptability will be measured by a feedback questionnaire as many times as the participant is willing to throughout the study. As of April 2024, a total of 47 participants have been enrolled and data from 1145 injecting events have already been collected, including 10 overdose events. This consists of 17 females and 30 males with an average age of 45 years. Data analysis is ongoing. This protocol establishes a foundation for advancing wearable technology in opioid overdose prevention within SIFs. The study will provide chest wall movement data and associated overdose data that will be used to train an algorithm that allows the biosensor to detect an overdose. The study will contribute crucial insights into OIRD, emphasizing the biosensor's potential step forward in real-time intervention strategies. DERR1-10.2196/57367.

A steady-state distributed parameter ultrathin heat pipe model considering the unsaturated wick

Ultrathin heat pipes have become mainstream to address the heat dissipation issues of small electronic devices. Numerous numerical studies based on the saturated wick assumption have been proposed to analyze their heat transfer performance. In order to achieve a more accurate assessment, this study presents a three-dimensional model that quantitatively reflects the relationship between capillary pressure and medium volume, along with a corresponding correlation. Based on this, a steady-state distributed parameter ultrathin heat pipe model considering the retreating of medium volume in the wick and the unsaturation vapor-liquid interface was developed, whose accuracy was verified by the experimental data. The results demonstrated that the capillary pressure increased with the decrease of medium volume and contact angle. The evolution of the overall meniscus with medium volume was categorized into two stages, where capillary pressure dependent on the main and micro meniscus at different stage. The medium volume distribution, corresponding to capillary pressure, will automatically adjust to match the overall medium pressure drop. Comparative analysis against experimental data, the proposed model reduced the maximum temperature difference error by 8.43 % compared to the saturated wick assumption at a heat load of 6 W, revealing that the unsaturated liquid wick model exhibited higher accuracy.

Analytical model of friction at low shear rates for soft materials in 3D printing.

The biological properties of silicone elastomers such as polydimethylsiloxane (PDMS) have widespread use in biomedicine for soft tissue implants, contact lenses, soft robots, and many other small medical devices, due to its exceptional biocompatibility. Additive manufacturing of soft materials still has significant challenges even with major advancements that have occurred in development of these technologies for customized medical devices and tissue engineering. The aim of this study was to develop a mathematical model of tangential stress in relation to shear stress, shear rate, 3D printing pressure and velocity, for non-Newtonian gels and fluids that are used as materials for 3D printing. This study used FENE (finitely extensible nonlinear elastic model) model, for non-Newtonian gels and fluids to define the dependences between tangential stress, velocity, and pressure, considering viscosity, shear stress and shear rates as governing factors in soft materials friction and adhesion. Experimental samples were fabricated as showcases, by SLA and FDM 3D printing technologies: elastic polymer samples with properties resembling elastic properties of PDMS and thermoplastic polyurethane (TPU) samples. Experimental 3D printing parameters were used in the developed analytical solution to analyse the relationships between governing influential factors (tangential stress, printing pressure, printing speed, shear rate and friction coefficient). Maple software was used for numerical modelling. Analytical model applied on a printed elastic polymer, at low shear rates, exhibited numerical values of tangential stress of 0.208-0.216 N m - 2 at printing velocities of 0.9 to 1.2 mm s - 1, while the coefficient of friction was as low as 0.09-0.16. These values were in accordance with experimental data in literature. Printing pressure did not significantly influence tangential stress, whereas it was slightly influenced by shear rate changes. Friction coefficient linearly increased with tangential stress. Simple analytical model of friction for elastic polymer in SLA 3D printing showed good correspondence with experimental literature data for low shear rates, thus indicating possibility to use it for prediction of printing parameters towards desired dimensional accuracy of printed objects. Further development of this analytical model should enable other shear rate regimes, as well as additional soft materials and printing parameters.

On-device training of artificial intelligence models on microcontrollers

&lt;p&gt;&lt;span&gt;Numerous studies are currently training artificial intelligence (AI) models on tiny devices constrained by computing power and memory limitations by implementing model optimization algorithms. The question arises whether implementing traditional AI models directly on small devices like micro-controller units (MCUs) is feasible. In this study, a library has been developed to train and predict the artificial neural network (ANN) model on common MCUs. The evaluation results on the regression problem indicate that, despite the extensive training time, when combined with multitasking programming on multi-core MCUs, the training does not adversely affect the system's execution. This research contributes an additional solution that enables the direct construction of ANN models on MCU systems with limited resources.&lt;/span&gt;&lt;/p&gt;

Symmetry and structure in the “Generalized Plasma Focus problem”

The “Generalized Plasma Focus problem” refers to a generic class of plasma propagation phenomena that share many features of a dense plasma focus device. Its recent theoretical development has been shown to predict some features of the pinch phase in PF-1000 and POSEIDON. The theory attempts to decompose the plasma propagation problem into two weakly interdependent subproblems. This is achieved by expressing every physical variable of an applicable continuum model of the plasma as the product of a scaling parameter, which contains device-related information and represents its numerical magnitude, and a scaled variable that is devoid of device-related information, is of order unity, and represents the spatiotemporal structure of that variable. The first subproblem seeks a traveling surface of revolution whose local normal velocity equals the scaling parameter for velocity and is aligned with the magnetic force density. Spatiotemporal distributions of all the scaled variables must move along with this reference surface by definition. This paper explores the resulting scaling theory and its symmetry properties. A new coordinate transformation results in a formula for the spatiotemporal distribution of the magnetic field of the curved and non-steady plasma sheath. New insights into the snowplow effect are obtained. A current sheath with a rear boundary exists only when the current is decreasing and only when the current carrying plasma is less dense than the fill gas. The current sheath thickness is the same for small and large devices. The geomagnetic flux compression problem has an exact solution.

Computational Modeling of Ganglion Cell Bicolor Opponent Receptive Fields and FPGA Adaptation for Parallel Arrays.

The biological system is not a perfect system, but it is a relatively complete system. It is difficult to realize the lower power consumption and high parallelism that characterize biological systems if lower-level information pathways are ignored. In this paper, we focus on the K, M and P pathways of visual signal processing from the retina to the lateral geniculate nucleus (LGN). We model the visual system at a fine-grained level to ensure efficient information transmission while minimizing energy use. We also implement a circuit-level distributed parallel computing model on FPGAs. The results show that we are able to transfer information with low energy consumption and high parallelism. The Artix-7 family of xc7a200tsbv484-1 FPGAs can reach a maximum frequency of 200 MHz and a maximum parallelism of 600, and a single receptive field model consumes only 0.142 W of power. This can be useful for building assistive vision systems for small and light devices.

Low-cost and scalable projected light-sheet microscopy for the high-resolution imaging of cleared tissue and living samples.

Light-sheet fluorescence microscopy (LSFM) is a widely used technique for imaging cleared tissue and living samples. However, high-performance LSFM systems are typically expensive and not easily scalable. Here we introduce a low-cost, scalable and versatile LSFM framework, which we named 'projected light-sheet microscopy' (pLSM), with high imaging performance and small device and computational footprints. We characterized the capabilities of pLSM, which repurposes readily available consumer-grade components, optimized optics, over-network control architecture and software-driven light-sheet modulation, by performing high-resolution mapping of cleared mouse brains and of post-mortem pathological human brain samples, and via the molecular phenotyping of brain and blood-vessel organoids derived from human induced pluripotent stem cells. We also report a method that leverages pLSM for the live imaging of the dynamics of sparsely labelled multi-layered bacterial pellicle biofilms at an air-liquid interface. pLSM can make high-resolution LSFM for biomedical applications more accessible, affordable and scalable.

Development of multi droplet-based electricity generator system for energy harvesting improvement from a single droplet

Abstract Due to high output performance, the droplet-based electricity generator (DEG) is garnering attention as a promising alternative power source for small electronic devices. Accordingly, to utilize the DEG as a power source, the efforts to boost the output have focused on methods to modify material modification and introduce surface structure. However, the behavior feature that the reconfigured droplet falls after the DEG operation leaves room for one more droplet energy harvesting from a single droplet. Here, a multi DEG system (MDEG) constructed with multiple DEG units is proposed to harvest more energy from a single droplet. The continuous movement of a water droplet is realized through the inclined stair structure of the MDEG, resulting in electrical energy generation from a single water droplet as many times as it falls. In particular, 2-step MDEG consisting of two DEG units can have 45% higher performance than a single DEG. Therefore, this study implies a contribution to the development of DEGs by considering the droplet dynamics, which has been overlooked in existing DEG studies.

An Evaluation of the Quality of Life and Emotional Status of Patients With Implantable Cardioverter Defibrillators at the Vilnius University Hospital, Lithuania.

Background and objective An arrhythmia is a disorder of the heart rate or rhythm. An implantable cardioverter defibrillator (ICD) is a small electronic device connected to the heart to continuously keep track of and help control rapid and sometimes life-threatening electrical problems with the heart. However, it may result in psychological tension in patients' lives, eventually affecting their quality of life (QoL). In light of this, we aimed to assess the QoL ofpatients with ICDat the Vilnius University Hospital, Lithuania. Methods We employed the following three questionnaires in this study: an originally prepared questionnaire including sociodemographic and health factors; the 36-item Short Form HealthSurvey questionnaire(SF-36); and the Hospital Anxiety and Depression Scale (HADS) to assess depression and anxiety. Data analysis was performed usingSPSS Statistics v. 13.00 (IBM Corp., Armonk, NY). Results Of the patients evaluated in all areas of QoL, males demonstrated higher scores compared to females. A statistically significant difference was found when assessing the domains of physical activity, social function, pain, and physical and mental health QoL. Conclusions Based on our findings, the majority of ICD patients can achieve their desired QoL and psychosocial outcomes. Our results show that patients with ICDs have the potential to return to a normal life. Depression and anxiety manifested more commonly inrespondents with a disease duration of up to five years.

Real-time auditory feedback for improving gait and walking in people with Parkinson’s disease: a pilot and feasibility trial

BackgroundTechnology is poised to bridge the gap between demand for therapies to improve gait in people with Parkinson’s and available resources. A wearable sensor, Heel2Toe™, a small device that attaches to the side of the shoe and gives a sound each time the person starts their step with a strong heel strike, has been developed and pre-tested by a team at McGill University. The objective of this study was to estimate feasibility and efficacy potential of the Heel2Toe™ sensor in changing walking capacity and gait pattern in people with Parkinson’s.MethodsA pilot study was carried out involving 27 people with Parkinson’s randomized 2:1 to train with the Heel2Toe[TM] sensor and or to train with recommendations from a gait-related workbook.ResultsA total of 21 completed the 3-month evaluation, 14 trained with the Heel2Toe[TM] sensor, and 7 trained with the workbook. Thirteen of 14 people in the Heel2Toe group improved over measurement error on the primary outcome, the 6-Minute Walk Test, (mean change 66.4 m) and 0 of the 7 in the Workbook group (mean change − 19.4 m): 4 of 14 in the Heel2Toe group made reliable change and 0 of 7 in the Workbook group. Improvements in walking distance were accompanied by improvements in gait quality. Forty percent of participants in the intervention group were strongly satisfied with their technology experience and an additional 37% were satisfied.ConclusionsDespite some technological difficulties, feasibility and efficacy potential of the Heel2Toe sensor in improving gait in people with Parkinson’s was supported.

Engineering acyclovir-induced RNA nanodevices for reversible and tunable control of aptamer function

Small molecule-regulated RNA devices have the potential to modulate diverse aspects of cellular function, but the small molecules used to date have potential toxicities limiting their use in cells. Here we describe a method for creating drug-regulated RNA nanodevices (RNs) using acyclovir, a biologically compatible small molecule with minimal toxicity. Our modular approach involves a scaffold comprising a central F30 three-way junction, an integrated acyclovir aptamer on the input arm, and a variable effector-binding aptamer on the output arm. This design allows for the rapid engineering of acyclovir-regulated RNs, facilitating temporal, tunable, and reversible control of intracellular aptamers. We demonstrate the control of the Broccoli aptamer and the iron-responsive element (IRE) by acyclovir. Regulating the IRE with acyclovir enables precise control over iron-regulatory protein (IRP) sequestration, consequently promoting the inhibition of ferroptosis. Overall, the method described here provides a platform for transforming aptamers into acyclovir-controllable antagonists against physiologic target proteins.