Functional composites by programming entropy-driven nanosheet growth.

Nanomaterials must be systematically designed to be technologically viable1-5. Driven by optimizing intermolecular interactions, current designs are too rigid to plug in new chemical functionalities and cannot mitigate condition differences during integration6,7. Despite extensive optimization of building blocks and treatments, accessing nanostructures with the required feature sizes and chemistries is difficult. Programming their growth across the nano-to-macro hierarchy also remains challenging, if not impossible8-13. To address these limitations, we should shift to entropy-driven assemblies to gain design flexibility, as seen in high-entropy alloys, and program nanomaterial growth to kinetically match target feature sizes to the mobility of the system during processing14-17. Here, following a micro-then-nano growth sequence in ternary composite blends composed of block-copolymer-based supramolecules, small molecules and nanoparticles, we successfully fabricate high-performance barrier materials composed of more than 200 stacked nanosheets (125 nm sheet thickness) with a defect density less than 0.056 µm-2 and about 98% efficiency in controlling the defect type. Contrary to common perception, polymer-chain entanglements are advantageous to realize long-range order, accelerate the fabrication process (<30 min) and satisfy specific requirements to advance multilayered film technology3,4,18. This study showcases the feasibility, necessity and unlimited opportunities to transform laboratory nanoscience into nanotechnology through systems engineering of self-assembly.

A Sparse Sampling Sensor Front-End IC for Low Power Continuous SpO 2 & HR Monitoring.

Photoplethysmography (PPG) is an attractive method to acquire vital signs such as heart rate and blood oxygenation and is frequently used in clinical and at-home settings. Continuous operation of health monitoring devices demands a low power sensor that does not restrict the device battery life. Silicon photodiodes (PD) and LEDs are commonly used as interface devices in PPG sensors; however, using of flexible organic devices can enhance the sensor conformality and reduce the cost of fabrication. In most PPG sensors, most of system power consumption is concentrated in powering LEDs, traditionally consuming mWs. Using organic devices further increases this power demand since these devices exhibit larger parasitic capacitances and typically need higher drive voltages.This work presents a sensor IC for continuous SpO 2 and HR monitoring that features an on-chip reconstruction-free sparse sampling algorithm to reduce the overall system power consumption by  âˆ¼ 70% while maintaining the accuracy of the output information. The designed frontend is compatible with a wide range of devices from silicon PDs to organic PDs with parasitic capacitances up to 10 nF. Implemented in a 40 nm HV CMOS process, the chip occupies 2.43 mm 2 and consumes 49.7 µW and 15.2 µW of power in continuous and sparse sampling modes respectively. The performance of the sensor IC has been verified in vivo with both types of devices and the results are compared against a clinical grade reference. Less than 1 bpm and 1% mean absolute errors were achieved in both continuous and sparse modes of operation.

Tuning Strain Sensor Performance via Programmed Thin-Film Crack Evolution.

Stretchable strain sensors with well-controlled sensitivity and stretchability are crucial for applications ranging from large deformation monitoring to subtle vibration detection. Here, based on single-metal material on the elastomer and one-pot evaporation fabrication method, we realize controlled strain sensor performance via a novel programable cracking technology. Specifically, through elastomeric substrate surface chemistry modification, the microcrack generation and morphology evolution of the strain sensing layer is controlled. This process allows for fine tunability of the cracked film morphology, resulting in strain sensing devices with a sensitivity gauge factor of over 10 000 and stretchability up to 100%. Devices with a frequency response up to 5.2 Hz and stability higher than 1000 cycles are reported. The reported strain sensors, tracking both subtle and drastic mechanical deformations, are demonstrated in healthcare devices, human-machine interaction, and smart-home applications.

A computational approach to mortality prediction of alcohol use disorder inpatients.

BACKGROUND: Health information technologies can assist clinicians in the Intensive Care Unit (ICU) by providing additional analysis of patient stability. However, because patient diagnoses can be confounded by chronic alcohol use, the predictive value of existing systems is suboptimal. Through the use of Electronic Health Records (EHR), we have developed computer software called AutoTriage to generate accurate predictions through multi-dimensional analysis of clinical variables. We analyze the performance of AutoTriage on the Alcohol Use Disorder (AUD) subpopulation in this study, and build on results we reported for AutoTriage performance on the general population in previous work. METHODS: AUD-related ICD-9 codes were used to obtain a patient population from MIMIC III ICU dataset for a retrospective study. Patient mortality risk score is generated through analysis of eight EHR-based clinical variables. The score is determined by combining weighted subscores, each of which are obtained from singlets, doublets or triplets of one or more of the eight continuous-valued clinical variable inputs. A temporally updating risk score is computed with a continuously revised 12-hour mortality prediction. RESULTS: Among AUD patients, in a non-overlapping test set, AutoTriage outperforms existing systems with an Area Under Receiver Operating Characteristic (AUROC) value of 0.934 for 12-h mortality prediction. At a sensitivity of 90%, AutoTriage achieves a specificity of 80%, positive predictive value of 40%, negative predictive value of 89%, and an Odds Ratio of 36. CONCLUSIONS: For mortality prediction, AutoTriage demonstrates improvements in both the accuracy and the Odds Ratio over current systems among the AUD patient population.