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While numerous studies have investigated the factors associated with autonomous delivery vehicles (ADVs), there remains a paucity of research concerning consumers’ intentions to utilize these technologies. Prior research has predominantly concentrated on the effects of individual variables on outcomes, often neglecting the synergistic influence of various factors on consumer intention. This study seeks to examine the collective impact of pro-environmental motives (including awareness of consequences and ascription of responsibility), normative motives (such as subjective norms and personal norms), risk factors (COVID-19 risk and delivery risk), and individual characteristics (including trust in technology and innovation) on consumers’ intentions to adopt ADVs. Employing a fuzzy-set qualitative comparative analysis (fsQCA), this research analyzed data from 561 Chinese consumers collected via an online platform. The results yielded six distinct solutions, indicating that multiple combinations of antecedent factors could lead to a higher intention to adopt compared to any singular factor. These findings offer significant theoretical and practical implications for the effective implementation of ADVs in the last-mile delivery sector.

A major challenge in addressing global health issues is developing simple, affordable biosensors with high sensitivity and specificity. Significant progress has been made in at-home medical detection kits, especially during the COVID-19 pandemic. Here, we demonstrated a coffee-ring biosensor with ultrahigh sensitivity, utilizing the evaporation of two sessile droplets and the formation of coffee-rings with asymmetric nanoplasmonic patterns to detect disease-relevant proteins as low as 3 pg/ml, under 12 min. Experimentally, a protein-laden droplet dries on a nanofibrous membrane, pre-concentrating biomarkers at the coffee ring. A second plasmonic droplet with functionalized gold nanoshells is then deposited at an overlapping spot and dried, forming a visible asymmetric plasmonic pattern due to distinct aggregation mechanisms. To enhance detection sensitivity, a deep neural model integrating generative and convolutional networks was used to enable quantitative biomarker diagnosis from smartphone photos. We tested four different proteins, Procalcitonin (PCT) for sepsis, SARS-CoV-2 Nucleocapsid (N) protein for COVID-19, Carcinoembryonic antigen (CEA) and Prostate-specific antigen (PSA) for cancer diagnosis, showing a working concentration range over five orders of magnitude. Sensitivities surpass equivalent lateral flow immunoassays by over two orders of magnitude using human saliva samples. The detection principle, along with the device, and materials can be further advanced for early disease diagnostics. Penketh et al. develop an approach for mapping the frequency response of thousands of meta-atoms in a microwave metasurface simultaneously, leading to the formation of detailed hyperspectral images. The approach – applicable to a wide range of metasurfaces – may overcome fabrication challenges for translation of such metasurfaces to real-world devices.

Flexible power sources with load bearing capability are attractive for modern wearable electronics. Here, free-standing supercapacitor fabrics that can store high electrical energy and sustain large mechanical loads are directly woven to be compatible with flexible systems. The prototype with reduced package weight/volume provides an impressive energy density of 2.58 mWh g −1 or 3.6 mWh cm −3 , high tensile strength of over 1000 MPa, and bearable pressure of over 100 MPa. The nanoporous thread electrodes are prepared by the activation of commercial carbon fibers to have three-orders of magnitude increase in the specific surface area and 86% retention of the original strength. The novel device configuration woven by solid electrolyte-coated threads shows excellent flexibility and stability during repeated mechanical bending tests. A supercapacitor watchstrap is used to power a liquid crystal display as an example of load-bearing power sources with various form-factor designs for wearable electronics.

Ultrathin transition metal carbides with high capacity, high surface area, and high conductivity are a promising family of materials for applications from energy storage to catalysis. However, large-scale, cost-effective, and precursor-free methods to prepare ultrathin carbides are lacking. Here, we demonstrate a direct pattern method to manufacture ultrathin carbides (MoC x , WC x , and CoC x ) on versatile substrates using a CO 2 laser. The laser-sculptured polycrystalline carbides (macroporous, ~10–20 nm wall thickness, ~10 nm crystallinity) show high energy storage capability, hierarchical porous structure, and higher thermal resilience than MXenes and other laser-ablated carbon materials. A flexible supercapacitor made of MoC x demonstrates a wide temperature range (−50 to 300 °C). Furthermore, the sculptured microstructures endow the carbide network with enhanced visible light absorption, providing high solar energy harvesting efficiency (~72 %) for steam generation. The laser-based, scalable, resilient, and low-cost manufacturing process presents an approach for construction of carbides and their subsequent applications. Transition metal carbides are attractive for electrochemical energy storage and catalysis, but cost effective preparation on a large scale is challenging. Here the authors use a direct pattern method to fabricate transition metal carbides for supercapacitors and solar energy harvesting for steam generation.

A variety of autonomous oscillations in nature such as heartbeats and some biochemical reactions have been widely studied and utilized for applications in the fields of bioscience and engineering. Here, we report a unique phenomenon of moisture-induced electrical potential oscillations on polymers, poly([2-(methacryloyloxy)ethyl] dimethyl-(3-sulfopropyl) ammonium hydroxide-co-acrylic acid), during the diffusion of water molecules. Chemical reactions are modeled by kinetic simulations while system dynamic equations and the stability matrix are analyzed to show the chaotic nature of the system which oscillates with hidden attractors to induce the autonomous surface potential oscillation. Using moisture in the ambient environment as the activation source, this self-excited chemoelectrical reaction could have broad influences and usages in surface-reaction based devices and systems. As a proof-of-concept demonstration, an energy harvester is constructed and achieved the continuous energy production for more than 15,000 seconds with an energy density of 16.8 mJ/cm 2 . A 2-Volts output voltage has been produced to power a liquid crystal display toward practical applications with five energy harvesters connected in series. Moisture-induced energy generation is a potential green energy power source. Here, the authors report a moisture-induced autonomous surface potential oscillation phenomenon and apply it to the demonstration of energy harvesters with long persistence time and good energy density

Three-dimensional (3D) additive manufacturing techniques have been utilized to make 3D electrical components, such as resistors, capacitors, and inductors, as well as circuits and passive wireless sensors. Using the fused deposition modeling technology and a multiple-nozzle system with a printing resolution of 30 μm, 3D structures with both supporting and sacrificial structures are constructed. After removing the sacrificial materials, suspensions with silver particles are injected subsequently solidified to form metallic elements/interconnects. The prototype results show good characteristics of fabricated 3D microelectronics components, including an inductor–capacitor-resonant tank circuitry with a resonance frequency at 0.53 GHz. A 3D “smart cap” with an embedded inductor–capacitor tank as the wireless passive sensor was demonstrated to monitor the quality of liquid food (e.g., milk and juice) wirelessly. The result shows a 4.3% resonance frequency shift from milk stored in the room temperature environment for 36 h. This work establishes an innovative approach to construct arbitrary 3D systems with embedded electrical structures as integrated circuitry for various applications, including the demonstrated passive wireless sensors. Three-dimensional microelectronics: Sensing spoilage in situ A three-dimensional (3D) printing technology makes possible arbitrary-shaped, integrated microelectronic components and circuitry with existing products such as food containers. Customizing microsystems through layer-by-layer manufacturing techniques is an attractive proposition. However, the polymers used typically offer poor conductivity, making them unsuitable for microelectronic device applications. Liwei Lin and colleagues from the USA and Hsinchu address this problem by printing resistor, capacitor, and inductor devices composed of hollow polymer tubes. By injecting silver paste into the tubes, curing the metal, and removing the polymer support, they are able to generate intricate yet functional 3D circuits. The team demonstrates the potential of their approach by creating a “smart cap”—a wireless inductive sensor incorporated into a milk carton lid. The sensor detects shifts in liquid dielectric constant signals to warn consumers about potential food safety issues.

Real‐time monitoring of vital sounds from cardiovascular and respiratory systems via wearable devices together with modern data analysis schemes have the potential to reveal a variety of health conditions. Here, a flexible piezoelectret sensing system is developed to examine audio physiological signals in an unobtrusive manner, including heart, Korotkoff, and breath sounds. A customized electromagnetic shielding structure is designed for precision and high‐fidelity measurements and several unique physiological sound patterns related to clinical applications are collected and analyzed. At the left chest location for the heart sounds, the S1 and S2 segments related to cardiac systole and diastole conditions, respectively, are successfully extracted and analyzed with good consistency from those of a commercial medical device. At the upper arm location, recorded Korotkoff sounds are used to characterize the systolic and diastolic blood pressure without a doctor or prior calibration. An Omron blood pressure monitor is used to validate these results. The breath sound detections from the lung/ trachea region are achieved a signal‐to‐noise ration comparable to those of a medical recorder, BIOPAC, with pattern classification capabilities for the diagnosis of viable respiratory diseases. Finally, a 6×6 sensor array is used to record heart sounds at different locations of the chest area simultaneously, including the Aortic, Pulmonic, Erb's point, Tricuspid, and Mitral regions in the form of mixed data resulting from the physiological activities of four heart valves. These signals are then separated by the independent component analysis algorithm and individual heart sound components from specific heart valves can reveal their instantaneous behaviors for the accurate diagnosis of heart diseases. The combination of these demonstrations illustrate a new class of wearable healthcare detection system for potentially advanced diagnostic schemes.

Recent years have seen the wide application of Location-Based Services (LBSs) in our daily life. Although users can enjoy many conveniences from the LBSs, they may lose their trajectory privacy when their location data are collected. Therefore, it is urgent to protect the user’s trajectory privacy while providing high quality services. Trajectory k-anonymity is one of the most important technologies to protect the user’s trajectory privacy. However, the user’s attributes are rarely considered when constructing the k-anonymity set. It results in that the user’s trajectories are especially vulnerable. To solve the problem, in this paper, a Spatiotemporal Mobility (SM) measurement is defined for calculating the relationship between the user’s attributes and the anonymity set. Furthermore, a trajectory graph is designed to model the relationship between trajectories. Based on the user’s attributes and the trajectory graph, the SM based trajectory privacy-preserving algorithm (MTPPA) is proposed. The optimal k-anonymity set is obtained by the simulated annealing algorithm. The experimental results show that the privacy disclosure probability of the anonymity set obtained by MTPPA is about 40% lower than those obtained by the existing algorithms while the same quality of services can be provided.

The poor gas selectivity problem has been a long-standing issue for miniaturized chemical-resistor gas sensors. The electronic nose (e-nose) was proposed in the 1980s to tackle the selectivity issue, but it required top-down chemical functionalization processes to deposit multiple functional materials. Here, we report a novel gas-sensing scheme using a single graphene field-effect transistor (GFET) and machine learning to realize gas selectivity under particular conditions by combining the unique properties of the GFET and e-nose concept. Instead of using multiple functional materials, the gas-sensing conductivity profiles of a GFET are recorded and decoupled into four distinctive physical properties and projected onto a feature space as 4D output vectors and classified to differentiated target gases by using machine-learning analyses. Our single-GFET approach coupled with trained pattern recognition algorithms was able to classify water, methanol, and ethanol vapors with high accuracy quantitatively when they were tested individually. Furthermore, the gas-sensing patterns of methanol were qualitatively distinguished from those of water vapor in a binary mixture condition, suggesting that the proposed scheme is capable of differentiating a gas from the realistic scenario of an ambient environment with background humidity. As such, this work offers a new class of gas-sensing schemes using a single GFET without multiple functional materials toward miniaturized e-noses.Sensors: Graphene and machine learning sniff out gasesA sensor combined with machine learning algorithms makes an effective ‘electronic nose’ to distinguish different gases, according to research from the United States. The new approach, developed by a team led by Takeshi Hayasaka of the University of California, Berkeley, combines selectivity, low cost, and low power consumption without needing different materials to sense different gases. A graphene field effect transistor is used as a sensor, and four parameters of its conductivity profile are used as inputs to a machine learning classifier. With enough data, the classifier could identify water, methanol, and ethanol vapors. The researchers also showed that it could distinguish water and methanol in a mixture. These findings are an important step towards a miniaturized e-nose, which would be useful in areas such as environmental and safety monitoring, petrochemical processing, and other industries.

Limited by preparation time and ligand solubility, synthetic protocols for cyclodextrin‐based metal‐organic framework (CD‐MOF), as well as subsequent derived materials with improved stability and properties, still remains a challenge. Herein, an ultrafast, environmentally friendly, and cost‐effective microwave method is proposed, which is induced by graphene oxide (GO) to design CD‐MOF/GOs. This applicable technique can control the crystal size of CD‐MOFs from macro‐ to nanocrystals. CD‐MOF/GOs are investigated as a new type of supramolecular adsorbent. It can selectively adsorb the dye molecule methylene green (MG) owing to the synergistic effect between the hydrophobic nanocavity of CDs, and the abundant O‐containing functional groups of GO in the composites. Following high temperature calcination, the resulting N, S co‐doped porous carbons derived from CD‐MOF/GOs exhibit a high capacitance of 501 F g −1 at 0.5 A g −1 , as well as stable cycling stability with 90.1% capacity retention after 5000 cycles. The porous carbon exhibits good electrochemical performance due to its porous surface containing numerous electrochemically active sites after dye adsorption and carbonization. The design strategy by supramolecular incorporating a variety of active molecules into CD‐MOFs optimizes the properties of their derived materials, furthering development toward the fabrication of zeitgeisty and high‐performance energy storage devices.