The packing in molecular crystals is determined by intermolecular interactions. An understanding of these interactions would enable the design of systems exhibiting effective conductivity and superconductivity. By careful analysis of the shortest intermolecular interactions in the crystal combined with accurate ab‐initio calculatins it has been shown that CH…X and S…S are the two interactions most likely to influence the structures of these materials.
Scheme 2 range of analyte concentration. Thus, better tuning of hydrogen bond interactions will result in structure-breaking effects at lower analyte concentrations. VCH Rdagsgevell~chuft mhH 0-69469 Weinlirim, 1995 0935-9648/95/1212-1026 $ T 00+ 25,O Adv M a w 1995, 7, No 12
Mueller matrix polarimetry distinguishes the different origins of the reversible and irreversible chiroptical effects emerging in stirred solutions of J-aggregate nanoparticles: the reversible effect is due to an anisotropic ordering in the solution and the irreversible one is due to a bias from the racemic composition of intrinsically chiral structures.
Sweat secreted by the human eccrine sweat glands can provide valuable biomarker information during exercise in hot and humid conditions. Real-time noninvasive biomarker recordings are therefore useful for evaluating the physiological conditions of an athlete such as their hydration status during endurance exercise. In this work, we describe a platform that in-cludes different sweat biomonitoring prototypes of cost-effective, smart wearable devices for continuous biomonitoring of sweat during exercise. One prototype is based on conformable and disposable soft sensing patches with an integrated multisensor array requiring the integration of different sensors and printed sensors with their corresponding functionalization protocols on the same substrate. The second is based on silicon based sensors and paper microfluidics. Both platforms integrate a multi-sensor array for measuring sodium, potassium, and pH in sweat. We show preliminary results obtained from the multi-sensor prototypes placed on two athletes during exercise. We also show that the machine learning algorithms can predict the percentage of body weight loss during exercise from biomarkers such as heart rate and sweat sodium concentration collected over multiple subjects.
This work describes a multisensing wearable platform for monitoring biomarkers in sweat during the practice of exercise. Five electrochemical sensors for pH, potassium, sodium, chloride, and lactate were implemented in a flexible patch approach, together with a paper microfluidic component, to continuously measure sweat composition. The sensors are fabricated with silicon technologies: ion selective field effect transistors (ISFETs) for pH and ionic species; and a gold thin-film microelectrode for lactate. The latter includes a polymeric membrane based on an electropolymerized polypyrroled structure, where all the biocomponents required for carrying out the lactate analyses are entrapped. The flexible patch is fabricated using hybrid integration technologies, including printed pads defined on a polyimide (Kapton®) substrate and wire bonding encapsulation of silicon chips. To fix and align the sensors to the flexible substrate, different laminated materials, such as polymethyl methacrylate (PMMA), polydimethylsiloxane (PDMS), and silicone-based adhesive, were used. The first results show good performance of the sensors—ISFETS sensitivity between 54–59 mV dec−1 for ion ranges in sweat from 2 to 100 mM and lactate sensor sensitivity of −135 × 102 µA M−1 cm−2 for the range of 2–50 mM. The microfluidic platform has been tested in terms of adequate sensor wettability and rapid response during the time span of exercise activity (2 h) showing excellent results.
The development of diagnostic tools for measuring a wide spectrum of target analytes, from biomarkers to other biochemical parameters in biological fluids, has experienced a significant growth in the last decades, with a good number of such tools entering the market. Recently, a clear focus has been put on miniaturized wearable devices, which offer powerful capabilities for real-time and continuous analysis of biofluids, mainly sweat, and can be used in athletics, consumer wellness, military, and healthcare applications. Sweat is an attractive biofluid in which different biomarkers could be noninvasively measured to provide rapid information about the physical state of an individual. Wearable devices reported so far often provide discrete (single) measurements of the target analytes, most of them in the form of a yes/no qualitative response. However, quantitative biomarker analysis over certain periods of time is highly demanded for many applications such as the practice of sports or the precise control of the patient status in hospital settings. For this, a feasible combination of fluidic elements and sensor architectures has been sought. In this regard, this paper shows a concise overview of analytical tools based on the use of capillary-driven fluidics taking place on paper or fabric devices integrated with solid-state sensors fabricated by thick film technologies. The main advantages and limitations of the current technologies are pointed out together with the progress towards the development of functional devices. Those approaches reported in the last decade are examined in detail.
Liquid analysis is key to track conformity with the strict process quality standards of sectors like food, beverage, and chemical manufacturing. In order to analyse product qualities online and at the very point of interest, automated monitoring systems must satisfy strong requirements in terms of miniaturization, energy autonomy, and real time operation. Toward this goal, we present the first implementation of artificial taste running on neuromorphic hardware for continuous edge monitoring applications. We used a solid-state electrochemical microsensor array to acquire multivariate, time-varying chemical measurements, employed temporal filtering to enhance sensor readout dynamics, and deployed a rate-based, deep convolutional spiking neural network to efficiently fuse the electrochemical sensor data. To evaluate performance we created MicroBeTa (Microsensor Beverage Tasting), a new dataset for beverage classification incorporating 7 h of temporal recordings performed over 3 days, including sensor drifts and sensor replacements. Our implementation of artificial taste is 15× more energy efficient on inference tasks than similar convolutional architectures running on other commercial, low power edge-AI inference devices, achieving over 178× lower latencies than the sampling period of the sensor readout, and high accuracy (97%) on a single Intel Loihi neuromorphic research processor included in a USB stick form factor.
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