Recently, there has been an interest in the incorporation of buckypaper (BP), or carbon nanotube (CNT) membranes, in composite laminates. Research has shown that using BP in contrast to nanotube doped resin enables the introduction of a higher CNT weight fraction which offers multiple benefits including higher piezo resistivity for health monitoring applications and enhanced mechanical response for structural applications. However, their impact on the deformation and failure mechanisms of composite laminates has not been investigated thoroughly. Understanding these issues experimentally would require a carefully executed test plan involving a multitude of design parameters such as BP geometry and placement, material anisotropy and variability, and laminate stacking sequence. This paper presents a deep learning (DL)-based surrogate model for studying the mechanical response of hybrid carbon fiber reinforced polymer (CFRP) composite laminates with BP interleaves under various mechanical loads. The surrogate model utilizes a long short-term memory architecture implemented within a DL framework and predicts the laminate global response for a given configuration, geometry, and loading condition. The DL framework training and cross-validation are performed via data acquisition from a series of three-point bend tests conducted through finite element analysis (FEA) and in-house experiments, respectively. The model predictions show good agreement with FEA simulations and experimental results, where CFRP with two BP interleaves showed enhanced flexural strength and modulus over pristine samples. This enhancement can be attributed to the excellent crack retardation capabilities of CNTs, particularly in the interlaminar region.

Lablets are autonomous microscopic particles with programmable CMOS electronics that can control electrokinetic phenomena and electrochemical reactions in solution via actuator and sensor microelectrodes. In this paper, we describe the design and fabrication of optimized singulated lablets (CMOS3) with dimensions 140x140x50 micrometers carrying an integrated coplanar encapsulated supercapacitor as a rechargeable power supply. The lablets are designed to allow docking to one another or to a smart surface for interchange of energy, electronic information, and chemicals. The paper focusses on the digital and analog design of the lablets to allow significant programmable functionality in a microscopic footprint, including the control of autonomous actuation and sensing up to the level of being able to support a complete lablet self-reproduction life cycle, although experimentally this remains to be proven. The potential of lablets in autonomous sensing and control and for evolutionary experimentation are discussed.

The programmable investigation and control of chemical systems at the microscale has been an increasingly successful area in microsystem technology for over 25 years including our own work in lab-on-a-chip and microfluidics to approach electronic chemical cells [1-2]. These systems require and are limited by their physical connection (wires, tubes, pipetting) to the macroscopic control system, both for electrical and chemical interfacing. Wireless electronic systems, communicating using radio waves, although already advocated for smart dust [3-4] and implemented down to mm scales, are not yet effective at 100 µm scales and below, especially in aqueous solution where communication is damped, and also do not provide a solution for powering smart microscopic electronic particles in solution. Our approach is a novel and more chemically inspired one [5] - to take advantage of the mobility of microscopic particles which allows their docking to one another pairwise or to a smart microstructured surface (called the dock). It involves fully programmable CMOS electronic particles in contrast to other more restricted approaches such as plasmonic smart dust [6]. Electronic integration using CMOS has been optimized for high speed (GHz range) operation and high integration levels with feature sizes down to 30nm and below. However, for microscopic electronics, extremely low power operation is required (total average power, typically 1 nW for 1000s) by current microscopic charge storage limitations ( 2 µF using supercap technology), which is not consistent either with high frequency operation or the leakage currents associated with the finest scale transistors. Instead, low power operation has been achieved using 180nm technology and an especially designed slow clock [7] and custom transistor design. Electronic actuation of chemical reactions mostly requires switching of voltages on microelectrodes in aqueous solution, which typically have significant capacitances, as exploited in electrolyte capacitors.

Applications of Structural Health Monitoring (SHM) combined with Machine Learning (ML) techniques enhance real-time performance tracking and increase structural integrity awareness of civil, aerospace and automotive infrastructures. This SHM-ML synergy has gained popularity in the last years thanks to the anticipation of maintenance provided by arising ML algorithms and their ability of handling large quantities of data and considering their influence in the problem. In this paper we develop a novel ML nearest-neighbors-alike algorithm based on the principle of maximum entropy to predict fatigue damage (Palmgren-Miner index) in composite materials by processing the signals of Lamb Waves -- a non-destructive SHM technique -- with other meaningful features such as layup parameters and stiffness matrices calculated from the Classical Laminate Theory (CLT). The full data analysis cycle is applied to a dataset of delamination experiments in composites. The predictions achieve a good level of accuracy, similar to other ML algorithms, e.g. Neural Networks or Gradient-Boosted Trees, and computation times are of the same order of magnitude. The key advantages of our proposal are: (1) The automatic determination of all the parameters involved in the prediction, so no hyperparameters have to be set beforehand, which saves time devoted to hypertuning the model and also represents an advantage for autonomous, self-supervised SHM. (2) No training is required, which, in an \textit{online learning} context where streams of data are fed continuously to the model, avoids repeated training -- essential for reliable real-time, continuous monitoring.

It's been a difficult year for shoppers looking for cars, electronics and anything that requires a computer chip. A global semiconductor shortage has left many companies unable to fill orders or even finish products they've started assembling, clogging up warehouses and leaving a lack of inventory across the nation. Buying a new PlayStation 5 console remains nearly impossible. Several automakers have slowed down production in their factories, delaying shipments of new vehicles. It's even impacted more obscure products--just try to find an affordable dog washing booth these days.

Depending on the people you talk to, architects approach artificial intelligence (AI) with a range of anticipation, skepticism, or dread. Some say algorithms will handle drudge work and free designers to focus on the more creative aspects of their jobs. Others assert that AI won't live up to its hype--at least not in the near future--and will make only marginal improvements in the profession. And a third group worries that software that learns on its own will put a lot of architects out of work. Science fiction writers have been imagining robots that think like human beings for more than 100 years.

The joy, anxiety, and anticipation of getting a COVID vaccine in America culminates, quite anticlimactically, with a piece of white cardstock. Some have already lost their vaccine cards or never got them to begin with. Others have their names misspelled and crossed out on it. Many are having trouble reconciling how something so simple--and easily forged--can carry such import and weight. The White House has recently clarified that there will be no federal vaccine passport.

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