Lifeboat Foundation

A research team at Rice University led by James Tour, the T.T. and W.F. Chao Professor of Chemistry and professor of materials science and nanoengineering, is tackling the environmental issue of efficiently recycling lithium ion batteries amid their increasing use.

The team has pioneered a new method to extract purified active materials from battery waste as detailed in the journal Nature Communications on July 24. Their findings have the potential to facilitate the effective separation and recycling of valuable battery materials at a minimal fee, contributing to a greener production of electric vehicles (EVs).

“With the surge in battery use, particularly in EVs, the need for developing sustainable recycling methods is pressing,” Tour said.

Researchers develop fibers with nanoscale surface modifications that significantly improve fog water collection rates, offering a promising solution for freshwater scarcity.

Right off the bat, one of the biggest improvements is the weight of the 4,680 shell itself – down to 49g from the 70g weight of a gen 1 cell. Tesla has essentially optimized the shell, making it thinner, and reducing its internal complexity. They do this by welding the tabless electrode to the cell cap.

That weight reduction is significant – at the battery pack level, the Cybertruck has 1,344 cells – which means that it reduces 28.2kg or 62.1lb of the overall pack weight. But rather than leaving that space empty, Tesla has instead filled that weight with more battery material. Calculated, that’s about a 10% increase in overall pack energy density.

Continue reading “A Look at Tesla’s 4680 Gen 2 Battery Cell” »

Identifying one faulty turbine in a wind farm, which can involve looking at hundreds of signals and millions of data points, is akin to finding a needle in a haystack.

Engineers often streamline this complex problem using deep-learning models that can detect anomalies in measurements taken repeatedly over time by each turbine, known as time-series data.

But with hundreds of wind turbines recording dozens of signals each hour, training a deep-learning model to analyze time-series data is costly and cumbersome. This is compounded by the fact that the model may need to be retrained after deployment, and wind farm operators may lack the necessary machine-learning expertise.

Nanomaterials, with their distinctive physical and chemical properties, hold significant promise for revolutionizing the housing construction industry. By enabling the development of stronger, more durable, efficient, and sustainable structures, nanotechnology offers solutions to challenges such as climate change and global urbanization.

The use of nanomaterials in construction began in the mid-1980s with the advent of carbon-based structures. Since then, their application has become more widespread, driving innovations in the sector. Today, advances in nanotechnology are leading to the creation of increasingly sophisticated, selective, and efficient nanomaterials, broadening the scope of construction capabilities.

This study explored the application of various nanomaterials—titanium dioxide, carbon nanotubes (CNTs), nanosilica, nanocellulose, nanoalumina, and nanoclay—in residential construction. These materials were chosen for their potential to enhance the structural integrity, thermal performance, and overall functionality of building materials used in housing.

The Driving Training Based Optimization (DTBO) algorithm, proposed by Mohammad Dehghani, is one of the novel metaheuristic algorithms which appeared in 2022⁸⁰. This algorithm is founded on the principle of learning to drive, which unfolds in three phases: selecting an instructor from the learners, receiving instructions from the instructor on driving techniques, and practicing newly learned techniques from the learner to enhance one’s driving abilities^81,82. In this work, DTBO algorithm is used, due to its effectiveness, which was confirmed by a comparative study⁸³ with other algorithms, including particle swarm optimization⁸⁴, Gravitational Search Algorithm (GSA)⁸⁵, teaching learning-based optimization, Gray Wolf Optimization (GWO)⁸⁶, Whale Optimization Algorithm (WOA)⁸⁷, and Reptile Search Algorithm (RSA)⁸⁸. The comparative study has been done using various kinds of benchmark functions, such as constrained, nonlinear and non-convex functions.

Lyapunov-based Model Predictive Control (LMPC) is a control approach integrating Lyapunov function as constraint in the optimization problem of MPC^89,90. This technique characterizes the region of the closed-loop stability, which makes it possible to define the operating conditions that maintain the system stability^91,92. Since its appearance, the LMPC method has been utilized extensively for controlling a various nonlinear systems, such as robotic systems⁹³, electrical systems⁹⁴, chemical processes⁹⁵, and wind power generation systems⁹⁰. In contrast to the LMPC, both the regular MPC and the NMPC lack explicit stability restrictions and can’t combine stability guarantees with interpretability, even with their increased flexibility.

The proposed method, named Lyapunov-based neural network model predictive control using metaheuristic optimization approach (LNNMPC-MOA), includes Lyapunov-based constraint in the optimization problem of the neural network model predictive control (NNMPC), which is solved by the DTBO algorithm. The suggested controller consists of two parts: the first is responsible for calculating predictions using a neural network model of the feedforward type, and the second is responsible to resolve the constrained nonlinear optimization problem using the DTBO algorithm. This technique is suggested to solve the nonlinear and non-convex optimization problem of the conventional NMPC, ensure on-line optimization in reasonable time thanks to their easy implementation and guaranty the stability using the Lyapunov function-based constraint. The efficiency of the proposed controller regarding to the accuracy, quickness and robustness is assessed by taking into account the speed control of a three-phase induction motor, and its stability is mathematically ensured using the Lyapunov function-based constraint. The acquired results are compared to those of NNMPC based on DTBO algorithm (NNMPC-DTBO), NNMPC using PSO algorithm (NNMPC-PSO), Fuzzy Logic controller optimized by TLBO (FLC-TLBO) and optimized PID controller using PSO algorithm (PID-PSO)⁹⁵.

“Establishing that there is a big reservoir of liquid water provides some window into what the climate was like or could be like,” said Dr. Michael Manga.

While Mars is incapable of having liquid water on its surface, what about underground, and how much could there be? This is what a recent study published in the Proceedings of the National Academy of Sciences hopes to address as a team of researchers investigated how liquid water might be present beneath the Martian surface. This study holds the potential to help researchers not only better understand the current conditions on the Red Planet, but also if these same conditions could have led to life existing on the surface in the past.

For the study, the researchers analyzed seismic data obtained by NASA’s now-retired InSight lander, which landed on Mars in 2018 and sent back valuable data regarding the interior of Mars until the mission ended in 2022. This was after mission planners determined the amount of dust that had collected on the lander’s solar panels did not allow for sufficient solar energy to keep it functioning. However, despite being expired for two years, scientists continued to pour over vast amounts of data regarding the interior of Mars.

Continue reading “Probing Mars’ Interior Reveals Vast Underground Water Reservoir” »

Inventing a new, faster way to produce sustainable, self-dyed leather alternatives is a major achievement for synthetic biology and sustainable fashion. Professor Tom Ellis

Synthetic chemical dyeing is one of the most environmentally toxic processes in fashion, and black dyes – especially those used in colouring leather – are particularly harmful. The researchers at Imperial set out to use biology to solve this.

How can older trees help combat climate change? This is what a recent study published in Nature Climate Change hopes to address as an international team of researchers investigated changes in woody biomass in older trees that have been while exposed to free-air CO₂ enrichment (FACE) resulting from climate change. This study holds the potential to help researchers, climate scientists, and the public better understand the steps that can be taken to decrease CO₂ emissions and combat climate change worldwide.

For the study, the researchers, led by the University of Birmingham’s Institute of Forest Research (BIFoR), conducted a FACE experiment through a combination of canopy laser scanning and tree-ring analysis to examine the 180-year-old Quercus robur L. woodland in central England between 2021and 2022. The goal was ascertaining the effectiveness of older trees compared to younger trees regarding their consumption of CO₂, also known as CO₂ storage. In the end, the researchers found increased levels of CO₂ compared to ambient conditions in 2021 and 2022, respectively, equivalent to 1.7 tons of dry matter per hectare per year.

“Our findings refute the notion that older, mature forests cannot respond to rising levels of atmospheric CO₂, but how they respond will likely depend on the supply of nutrients from the soil,” said Dr. Richard Norby from the University of Birmingham, who is lead author of the study. “Evidence from BIFoR FACE of a significant increase in woody biomass production supports the role of mature, long-established, forests as natural climate solutions in the coming decades while society strives to reduce its dependency on carbon.”