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Abstract Despite the extensive developments of flexible capacitive pressure sensors, it is still elusive to simultaneously achieve excellent linearity over a broad pressure range, high sensitivity, and ultrahigh pressure resolution under large pressure preloads. Here, we present a programmable fabrication method for microstructures to integrate an ultrathin ionic layer. The resulting optimized sensor exhibits a sensitivity of 33.7 kPa −1 over a linear range of 1700 kPa, a detection limit of 0.36 Pa, and a pressure resolution of 0.00725% under the pressure of 2000 kPa. Taken together with rapid response/recovery and excellent repeatability, the sensor is applied to subtle pulse detection, interactive robotic hand, and ultrahigh-resolution smart weight scale/chair. The proposed fabrication approaches and design toolkit from this work can also be leveraged to easily tune the pressure sensor performance for varying target applications and open up opportunities to create other iontronic sensors. 

Size-selected negatively-charged boron clusters (B n − ) have been found to be planar or quasi-planar in a wide size range. Even though cage structures emerged as the global minimum at B 39 − , the global minimum of B 40 − was in fact planar. Only in the neutral form did the B 40 borospherene become the global minimum. How the structures of larger boron clusters evolve is of immense interest. Here we report the observation of a bilayer B 48 − cluster using photoelectron spectroscopy and first-principles calculations. The photoelectron spectra of B 48 − exhibit two well-resolved features at low binding energies, which are used as electronic signatures to compare with theoretical calculations. Global minimum searches and theoretical calculations indicate that both the B 48 − anion and the B 48 neutral possess a bilayer-type structure with D 2h symmetry. The simulated spectrum of the D 2h B 48 − agrees well with the experimental spectral features, confirming the bilayer global minimum structure. The bilayer B 48 −/0 clusters are found to be highly stable with strong interlayer covalent bonding, revealing a new structural type for size-selected boron clusters. The current study shows the structural diversity of boron nanoclusters and provides experimental evidence for the viability of bilayer borophenes. 

Although increasing efforts have been devoted to the development of non‐invasive wearable electrochemical sweat sensors for monitoring physiological and metabolic information, most of them still suffer from poor stability and specificity over time and fluctuating temperatures. This study reports the design and fabrication of a long‐term stable and highly sensitive flexible electrochemical sensor based on nanocomposite‐modified porous graphene by facile laser treatment for detecting biomarkers such as glucose in sweat. The laser‐reduced and patterned stable conductive nanocomposite on the porous graphene electrode provides the resulting glucose sensor with an excellent sensitivity of 1317.69 µA mm⁻¹cm⁻²and an ultra‐low limit of detection of 0.079 µm. The sensor can also detect pH and exhibit extraordinary stability to maintain more than 91% sensitivity over 21 days in ambient conditions. Taken together with a temperature sensor based on the same material system, the dual glucose and pH sensor integrated with a flexible microfluidic sweat sampling network further results in accurate continuous on‐body glucose detection calibrated by the simultaneously measured pH and temperature. The low‐cost, highly sensitive, and long‐term stable platform could facilitate the early identification and continuous monitoring of different biomarkers for non‐invasive disease diagnosis and treatment evaluation.

 

Previous research has noted that many factors greatly influence the spread of COVID‐19. Contrary to explicit factors that are measurable, such as population density, number of medical staff, and the daily test rate, many factors are not directly observable, for instance, culture differences and attitudes toward the disease, which may introduce unobserved heterogeneity. Most contemporary COVID‐19 related research has focused on modeling the relationship between explicitly measurable factors and the response variable of interest (such as the infection rate or the death rate). The infection rate is a commonly used metric for evaluating disease progression and a state's mitigation efforts. Because unobservable sources of heterogeneity cannot be measured directly, it is hard to incorporate them into the quantitative assessment and decision‐making process. In this study, we propose new metrics to study a state's performance by adjusting the measurable county‐level covariates and unobservable state‐level heterogeneity through random effects. A hierarchical linear model (HLM) is postulated, and we calculate two model‐based metrics—the standardized infection ratio (SDIR) and the adjusted infection rate (AIR). This analysis highlights certain time periods when the infection rate for a state was high while their SDIR was low and vice versa. We show that trends in these metrics can give insight into certain aspects of a state's performance. As each state continues to develop their individualized COVID‐19 mitigation strategy and ultimately works to improve their performance, the SDIR and AIR may help supplement the crude infection rate metric to provide a more thorough understanding of a state's performance.