Kirigami provides a powerful strategy to transform two-dimensional elements into complex three-dimensional functional structures with lengths ranging from nanoscale to microscale and macroscale. The stability and programmability of forming three-dimensional structures through mechanical actuation, whether external or self-balancing, are crucial. Here, we offer a system that performs the 2D to 3D transformation through sequential in-plane tension and release. As a result, the 3D state is obtained by out-plane popping and rotation and shows a self-locking behavior. The range of geometric parameters for kirigami elements with different stability properties is determined theoretically. The in-plane tension conditions are also calculated to break the transition point of the forming process. The horizontal and vertical modular array analysis demonstrates the scalability and programmability from the self-locking elements to the Kirigami surfaces. We expect that the kirigami pattern and design approach will serve for innovative systems, including tunable antennas, flexible electronics, and medical devices.
Time-series clustering is a powerful data mining technique for time-series data in the absence of prior knowledge of the clusters. Here we propose a time-series clustering method that leverages an annealing machine, which accurately solves combinatorial optimization problems. The proposed method facilitates an even classification of time-series data into closely located clusters while maintaining robustness against outliers. We compared the proposed method with an existing standard method for clustering an online distributed dataset and found that both methods yielded comparable results. Furthermore, the proposed method was applied to a flow measurement image dataset containing noticeable noise with a signal-to-noise ratio of approximately unity. Despite a small signal variation of approximately 2%, the proposed method effectively classified the data without any overlaps among the clusters. In contrast, the clustering results of the existing methods exhibited overlapping clusters. These results indicate the effectiveness of the proposed method.
Ultrashort pulse laser drilling is a promising method for the fabrication of microchannels in dielectric materials. Due to the complexity of the process, there is a strong demand for numerical models (simulators) that can predict structures produced under specific processing conditions in order to rapidly find optimal processing parameters. However, the validity of conventional laser drilling simulators for dielectrics has been confined to a range of strict interpolations of the data used during the construction of the model, and thus, their usefulness is limited. Here, we demonstrate simulator-based optimization for ultrashort pulse laser drilling in dielectrics based on an iterative deep neural network which is trained to predict microchannel structure after a small number of irradiated pulses. Our approach predicts the development of hole shapes over a wide variety of conditions and allowed the discovery of 20% more energy efficient processing strategies than in the initial experimental data. More broadly, our approach can address realistic problems in laser processing considering a variety of processing parameters, and thus enabling improved performance of next-generation smart laser processing systems.
Development of cementitious materials with low carbon footprint is critical for greenhouse gas mitigation. Coal fly ash (CFA) is an attractive diluent additive in cement due to its widespread availability and ultralow cost, but the heavy metals in CFA could leach out over time. Traditional acid washing processes for heavy metal removal suffer from high chemical consumption and high-volume wastewater streams. Here, we report a rapid and water-free process based on flash Joule heating (FJH) for heavy metals removal from CFA. The FJH process ramps the temperature to ~3000 °C within one second by an electric pulse, enabling the evaporative removal of heavy metals with efficiencies of 70–90% for arsenic, cadmium, cobalt, nickel, and lead. The purified CFA is partially substituted in Portland cement, showing enhanced strength and less heavy metal leakage under acid leaching. Techno-economic analysis shows that the process is energy-efficient with the cost of ~$21 ton −1 in electrical energy. Life cycle analysis reveals the reuse of CFA in cement reduces greenhouse gas emissions by ~30% and heavy metal emissions by ~41%, while the energy consumption is balanced, when compared to landfilling. The FJH strategy also works for decontamination of other industrial wastes such as bauxite residue.
Cone beam computed tomography (CBCT) is widely used in medical and dental imaging. Compared to a multidetector CT, it provides volumetric images with high isotropic resolution at a reduced radiation dose, cost and footprint without the need for patient translation. The current CBCT has several intrinsic limitations including reduced soft tissue contrast, inaccurate quantification of X-ray attenuation, image distortions and artefacts, which have limited its clinical applications primarily to imaging hard tissues and made quantitative analysis challenging. Here we report a multisource CBCT (ms-CBCT) which overcomes the short-comings of the conventional CBCT by using multiple narrowly collimated and rapidly scanning X-ray beams from a carbon nanotube field emission source array. Phantom imaging studies show that, the ms-CBCT increases the accuracy of the Hounsfield unit values by 60%, eliminates the cone beam artefacts, extends the axial coverage, and improves the soft tissue contrast-to-noise ratio by 30-50%, compared to the CBCT configuration.
In human movement augmentation, the number of controlled degrees of freedom could be enhanced by the simultaneous and independent use of supernumerary robotic limbs (SRL) and natural ones. However, this poses several challenges, that could be mitigated by encoding and relaying the SRL status. Here, we review the impact of supplementary sensory feedback on the control and embodiment of SRLs. We classify the main feedback features and analyse how they improve control performance. We report the feasibility of pushing body representation beyond natural human morphology and suggest that gradual SRL embodiment could make multisensory incongruencies less disruptive. We also highlight shared computational bases between SRL motor control and embodiment and suggest contextualizing them within the same theoretical framework. Finally, we argue that a shift towards long term experimental paradigms is necessary for successfully integrating motor control and embodiment.
Wearable devices for continuous non-invasive monitoring of physiological signals are crucial for preventive care and management of chronic conditions. However, these devices are either sensitive to skin conditions or require external stimuli, such as light or electrical excitation, to penetrate the skin for pulse detection. This often results in large motion artifacts and unsuitability for certain skin conditions. Here we demonstrate a simple fingertip-type device which can detect clear pulse signals under a range of conditions, including fingers covered by opaque substances such as a plaster or nail polish, or fingers immersed in water. The structure of the device consists of a pair of magnets and a magnetic sensor. Both experimental and numerical results show that the detected pulsation signals correspond directly to the vibrations induced by blood circulation with negligible influence from modulated magnetic signature of blood. Therefore, this device could be used in conditions which are challenging for existing pulse detectors and might also have its application extended to blood pressure measurement.
The COVID-19 pandemic has increased demand for point-of-care (POC) screening tests such as lateral flow assays (LFAs) and highlighted the need for sensitive and cost-effective POC diagnostic platforms. Here, we demonstrate an LFA platform using standard fluorescent nanoparticle reporters in which optical excitation is replaced by chemical excitation using the peroxyoxalate-based chemistry of inexpensive, shelf-stable glowsticks. The one-step chemi-excitation of fluorescent particles produces visible light readable by an unmodified smartphone, enhancing sensitivity while preserving simplicity and cost-effectiveness. Our Glow LFA detected the common model analyte human chorionic gonadotropin with a limit of detection (LoD) of 39 pg/mL-over ten times more sensitive than standard gold nanoparticles using the same antibodies. We also demonstrate its application to the detection of SARS-CoV-2 nucleoprotein at 100 pg/mL in nasal swab extract. Multiple fluorescent dyes can be chemi-excited by a single reagent, allowing for color multiplexing on a single LFA strip with a smartphone camera. The detection of three analytes on a single LFA test line was demonstrated using red, green, and blue fluorescent reporter particles, making glow LFA a promising platform for multiplexed detection.
Since the initial patent in 2001, the Magnetic Particle Imaging (MPI) community has been striving to develop an MPI scanner suitable for human applications. Numerous contributions from different research fields, regarding tracer development, reconstruction methods, hardware engineering, and sequence design have been employed in pursuit of this objective. In this work, we introduce and thoroughly characterize an improved head-sized MPI scanner with an emphasis on human safety. The scanner is operated by open-source software that enables scanning, monitoring, analysis, and reconstruction, designed to be handled by end users. Our primary focus is to present all technical components of the scanner, with the ultimate objective to investigate brain perfusion imaging in phantom experiments. We have successfully achieved full 3D single- and multi-contrast imaging capabilities at a frame rate of 4 Hz with sufficient sensitivity and resolution for brain applications. To assess system characterization, we devised sensitivity, resolution, perfusion, and multi-contrast experiments, as well as field measurements and sequence analysis. The acquired images were captured using a clinically approved tracer and suitable magnetic field strengths, while adhering to the established human peripheral nerve stimulation thresholds. This advanced scanner holds potential as a tomographic imager for diagnosing conditions such as ischemic stroke or intracranial hemorrhage in environments lacking electromagnetic shielding. Furthermore, due to its low power consumption it may have the potential to facilitate long-term monitoring within intensive care units for various applications.
Single-pixel imaging (SPI) has the advantages of high-speed acquisition over a broad wavelength range and system compactness. Deep learning (DL) is a powerful tool that can achieve higher image quality than conventional reconstruction approaches. Here, we propose a Bayesian convolutional neural network (BCNN) to approximate the uncertainty of the DL predictions in SPI. Each pixel in the predicted image represents a probability distribution rather than an image intensity value, indicating the uncertainty of the prediction. We show that the BCNN uncertainty predictions are correlated to the reconstruction errors. When the BCNN is trained and used in practical applications where the ground truths are unknown, the level of the predicted uncertainty can help to determine whether system, data, or network adjustments are needed. Overall, the proposed BCNN can provide a reliable tool to indicate the confidence levels of DL predictions as well as the quality of the model and dataset for many applications of SPI.
Cement-based construction 3D printing (C3DP) has the potential to be a climate solution by promoting cement decarbonization. Here we propose five policy actions that can guide C3DP toward becoming an emission abating tool.
Nanometre-thick, ultrathin coatings applied over a large area are of paramount importance for various application fields such as biomedicine, space and automotive, organic electronics, memory devices, or energy storage devices. So far wet chemical deposition as a cost-effective, scalable, and versatile method can only be used for thicker deposits. Here the formation of uniform ultra-thin coatings with thicknesses below 15 nm using a nature-inspired, roll-to-roll compatible Spray-on-Screen (SoS) technology is reported. For this, the finite micro-droplet generation of Ultrasonic Spray Coating (USSC) is combined with the coating formation from a screen printing mesh. Hydrophobic micro-threads of the mesh, resembling the micro-hair on the legs of water striders, produce millidroplets from micro droplets, and when applying an external pressure to the mesh, dynamic wetting is enforced. The proposed technology is applicable for a wide variety of substrates and applications. It is shown by theory and experiment that ultra-thin coatings below 5 nm homogeneous over a large area can be deposited without the use of extended ink formulation or high substrate temperatures during or after deposition. This simple yet effective technique enables the deposition of ultra-thin films on any substrates, and is very promising to fabricate the organic, inorganic electronics devices and batteries cost effectively.
Transport infrastructure allows society to function. Such systems continuously improve through manufacturing transformation and technology upgrading. However, its metabolism mechanism of material degradation and quantities from consumption to obsolescence remains unclear. Here we estimate the recycling potential of typical large-scale public transport vehicles (LPTV) in China, in particular, predicting the end-of-life quantity of railway and aviation equipment between 2000 and 2050. Their total recycling potential has been experiencing rapid growth. The total obsolescence mass in 2020 exceeded 33 million tons, and it is expected to reach another 74 million tons by 2050, roughly twice the amount in 2020. By 2050, waste LPTV in China will contain at least 72 million tons of steel, 838 kilotons of aluminum, 2539 tons of titanium, and 223 tons of neodymium. We also compare waste LPTV to e-waste and end-of-life private vehicles. Interestingly, their growth of generation quantity indicates a distinct industry succession from an industrial ecological perspective.
Orbital angular momentum (OAM), which was first discovered in the optical field, represents a new dimension of electromagnetic waves. However, the detection of OAM microwave photons, i.e., vortex microwave photons, at room temperature is difficult due to their low energy. Here we report a prototype of a vortex microwave photon detection device based on vortex electrons. Our OAM detection device efficiently distinguishes the intrinsic OAM in the microwave band, which is helpful for exploring new physical dimensions. In addition, the detection device can be enhanced with a vortex electron sorting device designed with electron holograms so that OAM microwave photon demultiplexing can be achieved. Finally, the OAM detection device has high practicability; i.e., not only it can be used at room temperature, but also it is much smaller than a particle accelerator system. To illustrate the significance of this method, we demonstrate an on-off keying transmission system based on our OAM detection device.
Rapid identification of antibiotic-resistant bacteria will play a key role in solving the global antibiotic crisis by providing a route to targeted antibiotic administration. However, current bacterial infection diagnoses take up to 3 days which can lead to antibiotic treatment that is less effective. Here we report a microfluidic system with a ladder shaped design allowing us to generate a twofold serial dilution of antibiotics comparable to current national and international standards. Our consolidated design, with minimal handling steps cuts down the testing time for antibiotic susceptibility from 16–20 h to 4–5 h. Our feasibility testing results are consistent with the commercial antibiotic susceptibility testing (AST) results, showing a 91.75% rate of agreement for Gram-negative and Gram-positive bacterial isolated from canine urinary tract infections (UTI) and may be used without prior isolation or enrichment. This platform provides an adaptable and efficient diagnostic tool for antibiotic susceptibility testing.
Abstract Today’s optical range finders or 3D imagers suffer from significant drawbacks and do not allow to combine performance (sensitivity, precision) with simplicity, and scalability enabling very-large scale integration with minimum footprint. Here, we present the amorphous silicon Intrinsic Photomixing Detector (IPD) for direct and highly sensitive optical envelope mixing. The ability to generate a photocurrent that is proportional to the nonlinear mixing of two optical modulation envelope functions enables high performance Time-of-Flight optical ranging at low light levels down to $$\sim \!0.1\,{{{{{\rm{mW}}}}}}{{{{{\rm{cm}}}}}}^{-2}$$ ~ 0.1 mW cm − 2 at $$444\,{{{{{\rm{nm}}}}}}$$ 444 nm . The IPD exceeds MHz bandwidth, covers a broad distance detection range from $$10\,{{{{{\rm{cm}}}}}}$$ 10 cm to $$101\,{{{{{\rm{m}}}}}}$$ 101 m , and achieves a mean depth resolution below $$44\,{{{{{\rm{mm}}}}}}$$ 44 mm for distances up to $$25\,{{{{{\rm{m}}}}}}$$ 25 m . The IPD paves the way towards simple but high-performance photodetectors that allow for very-large scale integration on top of silicon or flexible electronics at low costs with pixel fill factors up to 100%.
