Unique Sensors Research Articles

Improving viral annotation with artificial intelligence.

Viruses of bacteria, "phages," are fundamental, poorly understood components of microbial community structure and function. Additionally, their dependence on hosts for replication positions phages as unique sensors of ecosystem features and environmental pressures. High-throughput sequencing approaches have begun to give us access to the diversity and range of phage populations in complex microbial community samples, and metagenomics is currently the primary tool with which we study phage populations. The study of phages by metagenomic sequencing, however, is fundamentally limited by viral diversity, which results in the vast majority of viral genomes and metagenome-annotated genomes lacking annotation. To harness bacteriophages for applications in human and environmental health and disease, we need new methods to organize and annotate viral sequence diversity. We recently demonstrated that methods that leverage self-supervised representation learning can supplement statistical sequence representations for remote viral protein homology detection in the ocean virome and propose that consideration of the functional content of viral sequences allows for the identification of similarity in otherwise sequence-diverse viruses and viral-like elements for biological discovery. In this review, we describe the potential and pitfalls of large language models for viral annotation. We describe the need for new approaches to annotate viral sequences in metagenomes, the fundamentals of what protein language models are and how one can use them for sequence annotation, the strengths and weaknesses of these models, and future directions toward developing better models for viral annotation more broadly.

Self-Assembled TiN-Metal Nanocomposites Integrated on Flexible Mica Substrates towards Flexible Devices.

The integration of nanocomposite thin films with combined multifunctionalities on flexible substrates is desired for flexible device design and applications. For example, combined plasmonic and magnetic properties could lead to unique optical switchable magnetic devices and sensors. In this work, a multiphase TiN-Au-Ni nanocomposite system with core-shell-like Au-Ni nanopillars embedded in a TiN matrix has been demonstrated on flexible mica substrates. The three-phase nanocomposite film has been compared with its single metal nanocomposite counterparts, i.e., TiN-Au and TiN-Ni. Magnetic measurement results suggest that both TiN-Au-Ni/mica and TiN-Ni/mica present room-temperature ferromagnetic property. Tunable plasmonic property has been achieved by varying the metallic component of the nanocomposite films. The cyclic bending test was performed to verify the property reliability of the flexible nanocomposite thin films upon bending. This work opens a new path for integrating complex nitride-based nanocomposite designs on mica towards multifunctional flexible nanodevice applications.

Microsecond-Scale Transient Thermal Sensing Enabled by Flexible Mo1-xWxS2 Alloys.

Real-time thermal sensing through flexible temperature sensors in extreme environments is critically essential for precisely monitoring chemical reactions, propellant combustions, and metallurgy processes. However, despite their low response speed, most existing thermal sensors and related sensing materials will degrade or even lose their sensing performances at either high or low temperatures. Achieving a microsecond response time over an ultrawide temperature range remains challenging. Here, we design a flexible temperature sensor that employs ultrathin and consecutive Mo1-x W x S2 alloy films constructed via inkjet printing and a thermal annealing strategy. The sensing elements exhibit a broad work range (20 to 823 K on polyimide and 1,073 K on flexible mica) and a record-low response time (about 30 μs). These properties enable the sensors to detect instantaneous temperature variations induced by contact with liquid nitrogen, water droplets, and flames. Furthermore, a thermal sensing array offers the spatial mapping of arbitrary shapes, heat conduction, and cold traces even under bending deformation. This approach paves the way for designing unique sensitive materials and flexible sensors for transient sensing under harsh conditions.

Practical organic electronic noses using semi-permeable polymer membranes

Electronic noses (E-noses) mimic olfactory organs and quantify volatile organic compounds (VOCs), emulating the sense of smell, with applications in real-time monitoring of VOCs in food, healthcare, and law enforcement industries. Organic field-effect transistor (OFET) based sensors can be fabricated in large arrays by economical processes, however, lack the selectivity necessary to differentiate wide varieties of VOCs. Here, we explore the use of permeable polymer membranes with OFET sensors to improve their selectivity. Acylated poly(vinyl alcohol) (PVA) derivatives were synthesized, characterized, and evaluated in OFET vapor sensors as tunable membranes. Sensors with and without acylated PVA membranes were tested with VOC analytes. By monitoring drain current over time, we found that membranes dramatically changed responses to different analytes. We show that VOCs including methanol, ethanol, hexane, toluene, tetrahydrofuran, methyl ethyl ketone, and ethyl acetate can all be detected and differentiated with much greater selectivity than sensors without membranes. In particular, methanol and ethanol, which are difficult to differentiate in vapor sensors, showed significant quantitative differences in the initial slope (0.0403 s−1 vs 0.0192 s−1 respectively; 6 σ) and decay constants (5.5 s vs 15.9 s respectively; 26 σ) of their current vs time responses, allowing methanol and ethanol to be differentiated with 100 % confidence. This approach enables the economical fabrication of large varieties of unique organic vapor sensors by simply changing the membrane layer and results in an outstanding level of selectivity for OFET vapor sensors.

Objective and Automated Quantification of Instrument Handling for Open Surgical Suturing Skill Assessment: A Simulation-Based Study.

Goal: Vascular surgical procedures are challenging and require proficient suturing skills. To develop these skills, medical training simulators with objective feedback for formative assessment are gaining popularity. As hardware advancements offer more complex, unique sensors, determining effective task performance measures becomes imperative for efficient suturing training. Methods: 97 subjects of varying clinical expertise completed four trials on a suturing skills measurement and feedback platform (SutureCoach). Instrument handling metrics were calculated from electromagnetic motion trackers affixed to the needle driver. Results: The results of the study showed that all metrics significantly differentiated between novices (no medical experience) from both experts (attending surgeons/fellows) and intermediates (residents). Rotational motion metrics were more consistent in differentiating experts and intermediates over traditionally used tooltip motion metrics. Conclusions: Our work emphasizes the importance of tool motion metrics for open suturing skills assessment and establishes groundwork to explore rotational motion for quantifying a critical facet of surgical performance.

Multifunctional Photosensitizers for PDT and Unique Fluorescent Sensors Based on the New Series of Cyanoaryl Porphyrazines with 4-(2-Propinyloxy)phenyl and 4-Benzyloxyphenyl Fragments in a Macrocycle Framing

Multifunctional Photosensitizers for PDT and Unique Fluorescent Sensors Based on the New Series of Cyanoaryl Porphyrazines with 4-(2-Propinyloxy)phenyl and 4-Benzyloxyphenyl Fragments in a Macrocycle Framing

Multi-modal deformation and temperature sensing for context-sensitive machines

Owing to the remarkable properties of the somatosensory system, human skin compactly perceives myriad forms of physical stimuli with high precision. Machines, conversely, are often equipped with sensory suites constituted of dozens of unique sensors, each made for detecting limited stimuli. Emerging high degree-of-freedom human-robot interfaces and soft robot applications are delimited by the lack of simple, cohesive, and information-dense sensing technologies. Stepping toward biological levels of proprioception, we present a sensing technology capable of decoding omnidirectional bending, compression, stretch, binary changes in temperature, and combinations thereof. This multi-modal deformation and temperature sensor harnesses chromaticity and intensity of light as it travels through patterned elastomer doped with functional dyes. Deformations and temperature shifts augment the light chromaticity and intensity, resulting in a one-to-one mapping between stimulus modes that are sequentially combined and the sensor output. We study the working principle of the sensor via a comprehensive opto-thermo-mechanical assay, and find that the information density provided by a single sensing element permits deciphering rich and diverse human-robot and robot-environmental interactions.

Riverine litter monitoring from multispectral fine pixel satellite images

Mismanaged litter or debris in aquatic systems can pose threats to water quality and blue economic activities. Monitoring strategies like remote sensing can complement and support the gathering of relevant descriptors useful for understanding challenges related to leakage litter. We present a robust technique for detecting and quantifying floating riverine litter, a soup of natural and anthropogenic materials. Spectral information of GeoEye, PlanetScope and Skysat fine resolution satellite imagery was statistically transformed into spatial anomalies correlated to fractional-pixel riverine litter abundance. Algorithm development also involved techniques that accounted for variation in satellite data characteristics. The detected fractional abundance was converted into a unit surface area in a region-of-interest. Intercomparison of derived area coverage from matching images captured by the various unique sensors in the PlanetScope and Skysat constellation were consistent (R² = 0.98). Likewise, the litter surface area derived manually had a very strong linear relationship to the algorithm estimates (R² > 0.99). The prospect of time series observations over several years at sub-daily to near daily intervals were also demonstrated using the cloud-free PlanetScope and Skysat imagery. Transferability as well as easy adaptation of the algorithm was further showcased by application over water bodies in Guatemala and Slovakia.

Yolk-shell composite optical sensors with chiral L-histidine/Rhodamine 6G for high-sensitivity “turn-on” detection of L-proline

Chirality is a ubiquitous phenomenon in nature and has attracted wide attention in the biomedicine, pharmaceutics and biosensing research fields. Enantiomeric recognition of chiral compounds, especially chiral drugs and chiral amino acids, is important for human health and nutrition. In this work, through the encapsulation of L-His&R6G (L-His = L-Histidine; R6G = Rhodamine 6G) into MOF@MOF framework ZIF-67@ZIF-8, composited material L-His&R6G@ZIF-67@ZIF-8 can be obtained. Additionally, through the etching process, a unique yolk-shell ZIF-8 chiral composite optical sensors L-His&R6G@ZIF-8 (1) can be successfully prepared. Photo-luminescent (PL) experiment also reveals that 1 can highly sensitively detect L-Proline (L-Pro) through the “turn-on” detection strategy (KBH = 1.22 × 104 M−1 and detection limit 1.9 μM). Further yolk-shell L-His&R6G@ZIF-8-based fabricate flexible mixed-matrix membranes has been prepared using doctor-blading technique, which show significant fluorescence enhancement effect under ultraviolet lamp. This work also provides the unique example of preparing chiral yolk-shell framework composite sensors, which have broad application in chiral sensing area.

Nanomolar detection of hypochlorite in ground water samples by a norbornene-based polymeric sensor via unusual fluorescence turn-on response

Hypochlorite anion has been widely used as a bleaching and disinfecting agent in daily life. Selective and sensitive identification ofOCl-ions from water is very important for researchers. For this purpose, monomeric (NPh), and polymeric (PNPh-Peg) novel fluorescent sensors have been established for the specific and excellently unique sensors that exhibit selective characteristics like excellent resistance to bleaching and a high fluorescence brightness. A multi-functional random polymer (PNPh-Peg) with ICT (intramolecular charge transfer) active para–amino phenol functionality and reactive oxygen species (ROS) responsive, PEG attached are readily prepared via ROMP (ring-opening metathesis polymerization).: An ICT-active random polymer (PNPh-Peg) exhibited an unexpectedly strong cyan blue emission in a water medium compared to that in other common organic solvents, which was dramatically increased by adding a trace amount of NaOCl.: Incorporating PEG moiety in the polymeric backbone increases the water solubility of a copolymer, and the ROS-responsive groups make the polymer a good ROS scavenger. Upon oxidation of the phenol group into carbonyl, both the monomeric (NPh) and polymeric (Norp-PEG oh) sensors showed a selective, noticeable, unusual fluorescence turn-on response towardsanalyteOCl-ions with a very fast response (within three minutes). The detection limit (59.14 nM) and (126.93 nM) were calculated for monomeric and polymeric sensors, respectively. This was a selective, specific oxidation reaction of the completely water-soluble random polymeric sensor (PNPh-Peg) for hypochlorite anions and can be applicable for quantitative measurement of aqueous OCl-. This ICT-active random polymeric molecule (PNPh-Peg) can also be used as a fluorescent sensor for unique OCl- detection from contaminated water by preparing a sensor-coated paper strip. Thus, these multi-functional monomeric (NPh) and polymeric (PNPh-Peg) sensors are anticipated to apply to the environment.

Simulation of Trenched Coreless Silica Fiber Sensor for Refractive Index Measurement

Coreless silica fiber has garnered a lot of attention in the field of sensors. Nevertheless, the limitation of conventional and unique coreless silica fiber sensors such as complex simulation, and high fragility of its physical structure needs to be considered. This work presents a simulation of a new coreless silica fiber design in a form of a trenched coreless silica fiber sensor to overcome these limitations. To optimize the sensor’s design, the trench depth was changed in the range of 0 μm to 50 µm at a fixed trench width of 50 μm. The change in analyte refractive index ranging from 1.3000 RIU to 1.4000 RIU was performed to evaluate the simulated sensing performance which showed that the increase in trench depth improved the sensor’s sensitivity with 50 μm being the trench depth at which the highest sensitivity was recorded. Later, the results obtained was compared to the closest in term of design which is the standard coreless fiber. Overall, the trenched coreless silica fiber design provided better sensitivity and is highly capable as of has a new refractive index sensor with an estimated sensitivity of up to 6.62726x10-7, higher than the simulated standard coreless fiber sensor with that of 2.22053x10-7.

Anti-swellable cellulose hydrogel for underwater sensing

Underwater sensing is of great significance in ocean exploration by divers to monitor their movements and keep in touch with the shore. However, unique sensors are required to apply in the marine environment that is quite different from the land circumstance. Herein, we reported a cellulose-skeleton-based composite hydrogel that is constraint to expand underwater under the effect of hydrogen bonds (H-bonds) and features advantages of high swelling resistance, structural durability, mechanical robustness, medium flexibility, high gauge factor (2.33) and long-term stability in water as a highly efficient wearable underwater sensor. This cellulose-based anti-swellable underwater hydrogel sensor showed tremendous potentials in underwater sensing applications for posture monitoring, communication, and marine biological research, etc.

Towards the History of Formation and Development of the Sverdlovsk Biotelemetry Group

The article discusses the main stages in the formation and the research and practical work of the Sverdlovsk Biotelemetry Group as a scientific school led by Professor V. V. Rozenblat. In the early 20th century, the development of physiology demanded fundamentally new medical and technical solutions for real-time recording of physiological data in free-moving biological objects. A new direction of medical science and technology – dynamic bioradiotelemetry – has emerged. An informal scientific association of engineers and doctors, the Sverdlovsk Biotelemetry Group, worked in Sverdlovsk from the mid-1950s through the 1970s. Its leader was Professor V. V. Rozenblat while L. S. Dombrovskii provided overall management of engineering work. From 1955 to 1960, the Group created the first reliable device for remote pulse recording via radio. The period from 1960 through 1970 was the time of the group’s highest activity and productivity. The number of its members increased to 20. More than 50 original devices, unique sensors, and transmitting and receiving devices as well as the optimal methods for capturing and recording biosignals and bioelectric currents have been developed. After 1970, the most significant achievement of the Group was the use of computer technology for automated collection and analysis of the results of bioradiotelemetry. A series of original instruments was used in the Soviet space exploration program. The Group’s efforts over the decades resulted in the substantiation of dynamic bioradiotelemetry as a set of technologies and methodologies for obtaining fundamentally new knowledge about human body functioning during various activities. A scientific basis was created for the development of space medicine and the main principles were developed for sports teams training and preparation, rehabilitation, labor regulations in various areas, and social assessment.

Facile construction of electrochemical and self-powered wearable pressure sensors based on metallic corrosion effects

Flexible mechanical sensors are essential components for smart wearables. Conventional resistive, capacitive, or transistor-based mechanical sensors consume energy continuously, while piezoelectric and triboelectric sensors respond selectively to dynamic or transient mechanical stimulations. Developing mechanical sensors that do not necessitate external power supply but are able to monitor static mechanical stimuli can compensate for the deficiencies of existing sensing devices. Here, we present the facile construction of a new paradigm of electrochemical mechanical sensors based on ubiquitous metallic corrosion effects. The intrinsic differences in corrosion activities of diverse metals (e.g., zinc, aluminum, copper, etc.) are utilized to create potential differences between two electrodes, followed by encoding external mechanical stimulations into the potential difference variations via carefully selected solid electrolytes. The developed electrochemical mechanical sensors exhibit comparable performance (e.g., sensitivity, recovery/response speed, reproducibility, etc.) with that of conventional sensors, but possess significantly superior simplicity, cost-efficiency, and desirable capability in resolving static or slowly-varying mechanical stimulations in a self-powered manner. As proof-of-concept demonstrations, machine learning enabled speech recognition with high accuracy of 99.07% and monitoring of diverse human physiological activities are successfully demonstrated. These proposed unique electrochemical mechanical sensors based on the ubiquitous metallic corrosion phenomena provide a simple but effective approach for the burgeoning human-machine interfacing requirements with great benefit to the resource efficiency and sustainability of our society.

Digital filtering dissemination for optimizing impedance cytometry signal quality and counting accuracy.

Improving biosensor performance which utilize impedance cytometry is a highly interested research topic for many clinical and diagnostic settings. During development, a sensor's design and external factors are rigorously optimized, but improvements in signal quality and interpretation are usually still necessary to produce a sensitive and accurate product. A common solution involves digital signal processing after sample analysis, but these methods frequently fall short in providing meaningful signal outcome changes. This shortcoming may arise from a lack of investigative research into selecting and using signal processing functions, as many choices in current sensors are based on either theoretical results or estimated hypotheses. While a ubiquitous condition set is improbable across diverse impedance cytometry designs, there lies a need for a streamlined and rapid analytical method for discovering those conditions for unique sensors. Herein, we present a comprehensive dissemination of digital filtering parameters applied on experimental impedance cytometry data for determining the limits of signal processing on signal quality improvements. Various filter orders, cutoff frequencies, and filter types are applied after data collection for highest achievable noise reduction. After designing and fabricating a microfluidic impedance cytometer, 9µm polystyrene particles were measured under flow and signal quality improved by6.09dB when implementing digital filtering. This approached was then translated to isolated human neutrophils, where similarly, signal quality improved by7.50dB compared to its unfiltered original data. By sweeping all filtering conditions and devising a system to evaluate filtering performance both by signal quality and object counting accuracy, this may serve as a framework for future systems to determine their appropriately optimized filtering configuration.

Computational Modelling of Doubly‐Photopolymerized Holographic Biosensors

AbstractHolographic sensors are optical devices capable of tuning reflection wavelength, dependent upon nanostructured variations in refractive index. Computational modelling is utilized to simulate the recording and swelling characteristics of developed doubly photopolymerized (DP) holographic sensors. The holographic devices simplify fabrication processes, reduce financial costs, and improve biocompatibility. A holographic grating is achieved through in situ photopolymerization of a highly crosslinked polymer to produce nanostructured refractive index modulation. The unique swelling characteristics DP holographic sensors possess necessitate the development of system‐specific computational modelling. Hydrogel parameters, including film thickness, refractive index change, layer number, and external medium refractive index are examined for their effect on reflection spectra. Optimized computational models are utilized to study the effect of differential swelling rates of individual layer spacings on sensor response, indicating an idealized reduction in a swelling of 50% for the highly crosslinked region. A 2D photonic crystal geometry with additional periodicity is developed, to inform further sensor design opportunities. Optimized parameters for both 1D and 2D photonic structures will assist the further development of DP holographic sensors.

Highly specific and selective fluorescent chemosensor for sensing of Hg(II) by NH-pyrazolate-functionalized AIEgens

Luminescent organic molecules are of important realistic significance to the human health and ecological environment due to their fascinating applications. Here we report the design and synthesis of luminescent organic-molecules by introducing two or four NH-pyrazolate groups as mercury-binding moieties to aromatic cores. Interestingly, the new aromatic tetraphenylene-bridged multi–NH–pyrazoles exhibit strong fluorescence in both aggregate and solid state and constitutes highly selective proof-of-concept luminescent sensor for Hg(II) ion among various competitive transition-metal ions in both organic and mixed solutions via metal-nitrogen binding. Especially, the present sensor including two NH-pyrazolyl groups showed an extremely high sensitivity with low limit of detection of 7.26 and 3.67 nM. The proposed design strategy provides a wide scope for the construction of unique turn-on sensors with substantial potential in the sense of heavy metal pollution in enviromental water samples.

Global maps of soil temperature.

Research in global change ecology relies heavily on global climatic grids derived from estimates of air temperature in open areas at around 2 m above the ground. These climatic grids do not reflect conditions below vegetation canopies and near the ground surface, where critical ecosystem functions occur and most terrestrial species reside. Here, we provide global maps of soil temperature and bioclimatic variables at a 1‐km2 resolution for 0–5 and 5–15 cm soil depth. These maps were created by calculating the difference (i.e. offset) between in situ soil temperature measurements, based on time series from over 1200 1‐km2 pixels (summarized from 8519 unique temperature sensors) across all the world's major terrestrial biomes, and coarse‐grained air temperature estimates from ERA5‐Land (an atmospheric reanalysis by the European Centre for Medium‐Range Weather Forecasts). We show that mean annual soil temperature differs markedly from the corresponding gridded air temperature, by up to 10°C (mean = 3.0 ± 2.1°C), with substantial variation across biomes and seasons. Over the year, soils in cold and/or dry biomes are substantially warmer (+3.6 ± 2.3°C) than gridded air temperature, whereas soils in warm and humid environments are on average slightly cooler (−0.7 ± 2.3°C). The observed substantial and biome‐specific offsets emphasize that the projected impacts of climate and climate change on near‐surface biodiversity and ecosystem functioning are inaccurately assessed when air rather than soil temperature is used, especially in cold environments. The global soil‐related bioclimatic variables provided here are an important step forward for any application in ecology and related disciplines. Nevertheless, we highlight the need to fill remaining geographic gaps by collecting more in situ measurements of microclimate conditions to further enhance the spatiotemporal resolution of global soil temperature products for ecological applications.

Emerging Biotechnology Markers of Cognitive Impairment

The early detection of cognitive impairment is among the National Institute on Aging’s (NIA) current research priorities. Sensor-based technologies have exploded in recent years allowing remote, continuous measurement of older adults’ free-living activity. This highly granular data has stimulated exciting new research exploring how change in health can be detected remotely using novel “biotechnology” markers. Yet, this area of research is in its infancy as it relates to predicting cognitive function. This symposium will provide an overview of the sensor-cognition research landscape and will feature 5 new studies exploring the relationship between biotechnology markers and cognitive function, each with unique sensors, cognitive measures and samples. The first three presentations will report associations between accelerometry-based activity measures (chest or wrist devices) and cognitive function (assessed by diagnosis, a neurocognitive assessment, or microstructural changes on DTI) in the Baltimore Longitudinal Study on Aging, a large, NIA-funded epidemiologic dataset. The fourth presentation will report the significance of free-living hip accelerometry activity measures beyond clinically-available information in a random forest prediction model of 1-year change in Montreal Cognitive Assessment scores among urban, predominantly African-American older adults without moderate-severe dementia residing in the community. The final presentation will report associations between room-to-room transitions as detected by in-home, infrared motion sensors and mild cognitive impairment using data from a community-dwelling sample of older adults residing alone. This symposium will provide a substantial expansion of current knowledge in this research space and will be relevant to clinicians or researchers with an interest in sensor technology or dementia.

A Mini-Survey and Feasibility Study of Deep-Learning-Based Human Activity Recognition from Slight Feature Signals Obtained Using Privacy-Aware Environmental Sensors

Numerous methods and applications have been proposed in human activity recognition (HAR). This paper presents a mini-survey of recent HAR studies and our originally developed benchmark datasets of two types using environmental sensors. For the first dataset, we specifically examine human pose estimation and slight motion recognition related to activities of daily living (ADL). Our proposed method employs OpenPose. It describes feature vectors without effects of objects or scene features, but with a convolutional neural network (CNN) with the VGG-16 backbone, which recognizes behavior patterns after classifying the obtained images into learning and verification subsets. The first dataset comprises time-series panoramic images obtained using a fisheye lens monocular camera with a wide field of view. We attempted to recognize five behavior patterns: eating, reading, operating a smartphone, operating a laptop computer, and sitting. Even when using panoramic images including distortions, results demonstrate the capability of recognizing properties and characteristics of slight motions and pose-based behavioral patterns. The second dataset was obtained using five environmental sensors: a thermopile sensor, a CO2 sensor, and air pressure, humidity, and temperature sensors. Our proposed sensor system obviates the need for constraint; it also preserves each subject’s privacy. Using a long short-term memory (LSTM) network combined with CNN, which is a deep-learning model dealing with time-series features, we recognized eight behavior patterns: eating, operating a laptop computer, operating a smartphone, playing a game, reading, exiting, taking a nap, and sitting. The recognition accuracy for the second dataset was lower than for the first dataset consisting of images, but we demonstrated recognition of behavior patterns from time-series of weak sensor signals. The recognition results for the first dataset, after accuracy evaluation, can be reused for automatically annotated labels applied to the second dataset. Our proposed method actualizes semi-automatic annotation, false recognized category detection, and sensor calibration. Feasibility study results show the new possibility of HAR used for ADL based on unique sensors of two types.