Progress and Perspectives of Mid-Infrared Photoacoustic Spectroscopy for Non-Invasive Glucose Detection.

The prevalence of diabetes is rapidly increasing worldwide and can lead to a range of severe health complications that have the potential to be life-threatening. Patients need to monitor and control blood glucose levels as it has no cure. The development of non-invasive techniques for the measurement of blood glucose based on photoacoustic spectroscopy (PAS) has advanced tremendously in the last couple of years. Among them, PAS in the mid-infrared (MIR) region shows great promise as it shows the distinct fingerprint region for glucose. However, two problems are generally encountered when it is applied to monitor real samples for in vivo measurements in this MIR spectral range: (i) low penetration depth of MIR light into the human skin, and (ii) the effect of other interfering components in blood, which affects the selectivity of the detection system. This review paper systematically describes the basics of PAS in the MIR region, along with recent developments, technical challenges, and data analysis strategies, and proposes improvements for the detection sensitivity of glucose concentration in human bodies. It also highlights the recent trends of incorporating machine learning (ML) to enhance the detection sensitivity of the overall system. With further optimization of the experimental setup and incorporation of ML, this PAS in the MIR spectral region could be a viable solution for the non-invasive measurement of blood glucose in the near future.

Dual quantum cascade lasers for noninvasive glucose detection using photoacoustic spectroscopy.

The combination of mid-infrared and photoacoustic spectroscopy has shown promising developments as a substitute for invasive glucose detection technology. A dual single-wavelength quantum cascade laser system has been developed using photoacoustic spectroscopy for noninvasive glucose monitoring. Biomedical skin phantoms with similar properties to human skin have been prepared with blood components at different glucose concentrations as test models for the setup. The detection sensitivity of the system has been improved to ± 12.5 mg/dL in the hyperglycemia blood glucose ranges. An ensemble machine learning classifier has been developed to predict the glucose level in the presence of blood components. The model, which was trained with 72,360 unprocessed datasets, achieved a 96.7% prediction accuracy with 100% of the predicted data located in zones A and B of Clarke's error grid analysis. These findings fulfill both the US Food and Drug Administration and Health Canada requirements for glucose monitors.

A Single Wavelength Mid-Infrared Photoacoustic Spectroscopy for Noninvasive Glucose Detection Using Machine Learning.

According to the International Diabetes Federation, 530 million people worldwide have diabetes, with more than 6.7 million reported deaths in 2021. Monitoring blood glucose levels is essential for individuals with diabetes, and developing noninvasive monitors has been a long-standing aspiration in diabetes management. The ideal method for monitoring diabetes is to obtain the glucose concentration level with a fast, accurate, and pain-free measurement that does not require blood drawing or a surgical operation. Multiple noninvasive glucose detection techniques have been developed, including bio-impedance spectroscopy, electromagnetic sensing, and metabolic heat conformation. Nevertheless, reliability and consistency challenges were reported for these methods due to ambient temperature and environmental condition sensitivity. Among all the noninvasive glucose detection techniques, optical spectroscopy has rapidly advanced. A photoacoustic system has been developed using a single wavelength quantum cascade laser, lasing at a glucose fingerprint of 1080 cm-1 for noninvasive glucose monitoring. The system has been examined using artificial skin phantoms, covering the normal and hyperglycemia blood glucose ranges. The detection sensitivity of the system has been improved to ±25 mg/dL using a single wavelength for the entire range of blood glucose. Machine learning has been employed to detect glucose levels using photoacoustic spectroscopy in skin samples. Ensemble machine learning models have been developed to measure glucose concentration using classification techniques. The model has achieved a 90.4% prediction accuracy with 100% of the predicted data located in zones A and B of Clarke's error grid analysis. This finding fulfills the US Food and Drug Administration requirements for glucose monitors.

Performance-Improved Highly Integrated Uniaxial Tristate Hybrid Nanogenerator for Sustainable Mechanical Energy Harvesting.

Despite advances in the development of individual nanogenerators, the level of output energy generation must be increased to meet the demands of commercial electronic systems and to broaden their scope of application. To harvest low-frequency ambient mechanical energy more efficiently, we proposed a highly integrated hybridized piezoelectric-triboelectric-electromagnetic (tristate) nanogenerator in a uniaxial structure. In its highly integrated approach, a piezoelectric nanogenerator (PENG) based on CsPbBr3 (cesium lead bromide) nanoparticles (NPs) and poly(dimethylsiloxane) (PDMS) nanocomposite was fabricated on a triboelectrically negative nanostructured polyimide (PI) substrate. A cylindrical aluminum electrode grooved with permanent magnets was directed to move along a spring-less metallic guide bounded by these nanocomposites, thus essentially forming two single-electrode mode triboelectric nanogenerators (TENGs). By its optimized material design and novel integration approach of the PENGs, TENGs, and electromagnetic generators (EMGs), this uniaxial tristate hybrid nanogenerator (UTHNG) can synergistically produce an instantaneous electrical power of 49 mW at low-frequency ambient vibration (5 Hz). The UTHNG has excellent charging characteristics, ramping up the output voltage of a 22 µF capacitor to 2.7 V in only 12 s, which is much faster than individual nanogenerators. This work will be a superior solution for harvesting low-frequency ambient energies by improving the performance of hybrid nanogenerators, potentially curtailing the technology gap for self-powered micro/nanosystems for the Internet of Things.

Sensing of ultraviolet light: a transition from conventional to self-powered photodetector.

Clouds in the sky pass almost 80% of ultraviolet (UV) radiation to the earth's surface, which has a significant impact on humankind. Conventional UV photodetectors (PDs) require an external battery, which not only increases the device size but also has a limited life span and maintenance costs can be prohibitively expensive. An alternative and more technically-sound solution would be the use of self-powered UV PDs that can operate independently, eliminating the need for an external source. Although many exciting studies have been done and state-of-the-art research is underway to successfully fabricate self-powered UV PDs, periodic reviews on this topic are deemed essential so that the technology's readiness can be properly evaluated and critical challenges can be addressed in a timely manner. In this article, the key issues and most exciting developments made in recent years on built-in electric field assisted self-powered UV PDs based on p-n homojunctions, p-n heterojunctions, and Schottky junctions followed by energy harvester integrated UV PDs are extensively reviewed. Finally, a summary and comparison of different types of self-powered UV PDs as well as future challenges that need to be addressed are discussed. This review sets a foundation providing essential insights into the present status of self-powered UV PDs with which researchers can engage and deal with the major challenges.

Single-wavelength water muted photoacoustic system for detecting physiological concentrations of endogenous molecules.

Based on the breakthrough technology of water muting on photoacoustic spectroscopy, a single wavelength photoacoustic system in the short-wavelength-infrared (SWIR) region was developed to sense the endogenous molecules (e.g. glucose, lactate, triglyceride, and serum albumin found in blood and interstitial fluid) in aqueous media. The system implemented a robust photoacoustic resonant cell that can significantly enhance the signal-to-noise ratio of the acoustic waves. The sensitivity of the system was explored, and the experimental results exhibit a precision detection of physiological concentrations of biomolecules by combining the techniques of water muting and photoacoustic resonant amplification in a portable and low-cost single wavelength laser system.

Noninvasive blood glucose detection using a quantum cascade laser.

A Quantum Cascade Laser (QCL) was invented in the late 90s as a promising mid-infrared light source and it has contributed to the fields of industry, military, medicine, and biology. The room temperature operation, watt-level output power, compact size, and wide tuning capability of this laser advanced the field of noninvasive blood glucose detection with the use of transmission, absorption, and photoacoustic spectroscopy. This review provides a complete overview of the recent progress and technical details of these spectroscopy techniques, using QCL as an infrared light source for detecting blood glucose concentrations in diabetic patients.