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Abstract For a continuous healthcare or environmental monitoring system, it is essential to reliably sense the analyte concentration reported by electrochemical sensors. However, environmental perturbation, sensor drift, and power-constraint make reliable sensing with wearable and implantable sensors difficult. While most studies focus on improving sensor stability and precision by increasing the system’s complexity and cost, we aim to address this challenge using low-cost sensors. To obtain the desired accuracy from low-cost sensors, we borrow two fundamental concepts from communication theory and computer science. First, inspired by reliable data transmission over a noisy communication channel by incorporating redundancy, we propose to measure the same quantity (i.e., analyte concentration) with multiple sensors. Second, we estimate the true signal by aggregating the output of the sensors based on their credibility, a technique originally developed for “truth discovery” in social sensing applications. We use the Maximum Likelihood Estimation to estimate the true signal and the credibility index of the sensors over time. Using the estimated signal, we develop an on-the-fly drift-correction method to make unreliable sensors reliable by correcting any systematic drifts during operation. Our approach can determine solution pH within 0.09 pH for more than three months by detecting and correcting the gradual drift of pH sensors as a function of gamma-ray irradiation. In the field study, we validate our method by measuring nitrate levels in an agricultural field onsite over 22 days within 0.06 mM of a high-precision laboratory-based sensor. We theoretically demonstrate and numerically validate that our approach can estimate the true signal even when the majority (~ 80%) of the sensors are unreliable. Moreover, by restricting wireless transmission to high-credible sensors, we achieve near-perfect information transfer at a fraction of the energy cost. The high-precision sensing with low-cost sensors at reduced transmission cost will pave the way for pervasive in-field sensing with electrochemical sensors. The approach is general and can improve the accuracy of any field-deployed sensors undergoing drift and degradation during operation.

The bifacial photovoltaics (PV) technology promises several advantages over monofacials, including improved energy yield, lower operating temperature, and easier integration with agrophotovoltaics. There have been various experimental or computational studies comparing bifacials to monofacials; however, a theory-experiment combined analysis for accurate worldwide extrapolation is missing. Literature review reveals that many reported experiments study standalone systems that overrepresent the yield performance obtainable in farms. Moreover, most reported experimental studies are for configurations that are not necessarily designed close to the optimum. In this work, we experimentally study and analyze the fixed-tilted bifacial farm configiurations, namely south-facing monofacial, south-facing tilted bifacial (TBF), and ground-sculpted vertical bifacial (VBF) arrays, at Dhaka, Bangladesh (23.7 °N, 90.4 °E). The optimal TBF configuration, for 0.5 albedo, yields 21.3% and 73.3% more than the optimal monofacial and the optimal VBF configurations, respectively. Through a combination of experimental and numerical analysis, we compare the in-array performance of the configurations under different albedo conditions to analyze the physics and consolidate the predictions. There is a growing interest in PV array configurations beyond the conventionally ground-mounted south-facing TBF, such as agrophotovoltaics, floating-PV, industry-roof PV array, etc. This necessitates a critical analysis of various array configurations for broader PV expansion.

Smart, ultra-scaled, always-on wearable, and implantable (WI) sensors are an exciting frontier in personalized medicine. These sensors integrate sensing and actuation capabilities, enabling real-time analyte detection for on-demand drug delivery, akin to a biological organ. The microneedle (MN)-based patch serves as a critical novel interface element in this system. It is inexpensive, minimally invasive, and safe, showing promise in glycemic management and insulin therapy in laboratory and animal studies. However, the current design of MNs relies primarily on empirical approaches, with significant challenges. These challenges include potential diffusion delays that may impede time-critical drug intervention and an iterative design process lacking a clear understanding of the tradeoff between the response time and the limits of detection. In this paper, we introduce the first predictive framework for MN sensors, based on physical scaling laws and biomimetic concepts. Our framework is supported by experimental and numerical validations, establishing analytical scaling relationships that capture the fundamental workings of hollow and porous-swellable MN sensors. It quantifies essential performance metrics like the ’response time (RT)’ and the ’limit of detection (LOD)’ while assessing the tradeoffs associated with various geometrical and physical parameters of the MN technology. As a result, our model provides a universal framework for interpreting/integrating the experimental findings reported by laboratories worldwide. By leveraging this predictive framework, researchers can advance the development and optimization of MN sensors, leading to improved performance and expanded applications in the field of wearable and implantable technologies.

Restricted Boltzmann Machine (RBM) is an energy-based, undirected graphical model. It is commonly used for unsupervised and supervised machine learning. Typically, RBM is trained using contrastive divergence (CD). However, training with CD is slow and does not estimate the exact gradient of the log-likelihood cost function. In this work, the model expectation of gradient learning for RBM has been calculated using a quantum annealer (D-Wave 2000Q), where obtaining samples is faster than Markov chain Monte Carlo (MCMC) used in CD. Training and classification results of RBM trained using quantum annealing are compared with the CD-based method. The performance of the two approaches is compared with respect to the classification accuracies, image reconstruction, and log-likelihood results. The classification accuracy results indicate comparable performances of the two methods. Image reconstruction and log-likelihood results show improved performance of the CD-based method. It is shown that the samples obtained from quantum annealer can be used to train an RBM on a 64-bit “bars and stripes” dataset with classification performance similar to an RBM trained with CD. Though training based on CD showed improved learning performance, training using a quantum annealer could be useful as it eliminates computationally expensive MCMC steps of CD.

Organometallic perovskite solar cells exhibit good efficiency but their photostability is still relatively poorly understood and controlled. Here the authors show that photo-degradation arises from the formation of light-activated meta-stable trap states, is reversible, and can be frozen at 0 °C.

The five senses of sight, sound, smell, touch, and taste define the world for us. Sensing technologies augment these primary sensing capabilities: Mechanical sensors broaden our perception of the world by being sensitive to objects that we cannot ‘sense’ and then translate it in a format so that we can. A false color-map of galaxies produced by a radio-telescope, the trajectories in a bubble-chamber of a subatomic particle, the sound of a carbon monoxide alarm, or an MRI image of the brain are all translations of information adapted specifically for us. It is hardly surprising, therefore, that the history of sensors parallels the development of science and technology.

Low cost, portable sensors can transform health care by bringing easily available diagnostic devices to low and middle income population, particularly in developing countries. Sample preparation, analyte handling and labeling are primary cost concerns for traditional lab-based diagnostic systems. Lab-on-a-chip (LoC) platforms based on droplet-based microfluidics promise to integrate and automate these complex and expensive laboratory procedures onto a single chip; the cost will be further reduced if label-free biosensors could be integrated onto the LoC platforms. Here, we review some recent developments of label-free, droplet-based biosensors, compatible with “open” digital microfluidic systems. These low-cost droplet-based biosensors overcome some of the fundamental limitations of the classical sensors, enabling timely diagnosis. We identify the key challenges that must be addressed to make these sensors commercially viable and summarize a number of promising research directions.