stable superconductivity ambient pressure

Physicists Achieve Stable Superconductivity at Ambient Pressure

Breakthrough in Ambient-Pressure Superconductivity

Researchers at the University of Houston's Texas Center for Superconductivity have reached another groundbreaking milestone in their pursuit of ambient-pressure high-temperature superconductivity, advancing the quest for superconductors that function in real-world conditions and paving the way for next-generation energy-efficient technologies.

Investigating Superconductivity in Bi₀.₅Sb₁.₅Te₃ (BST)

Research by Liangzi Deng and Paul Ching-Wu Chu

Professors Liangzi Deng and Paul Ching-Chu of the UH Department of Physics investigated the induction of superconductivity in Bi₀.₅Sb₁.₅Te₃ (BST) under pressure while preserving its chemical and structural properties, as detailed in their study, "Creation, stabilization, and investigation at ambient pressure of pressure-induced superconductivity in Bi₀.₅Sb₁.₅Te₃" published in the Proceeding of the National Academy of Sciences.

Link Between Pressure, Topology, and Superconductivity

"The idea that high-pressure treatment of BST might reconfigure its Fermi surface topology and enhance thermoelectric performance emerged in 2001," Deng stated. "That intricate relationship between pressure, topology and superconductivity drew our interest."

Challenges in High-Pressure Superconductors

Metastable States and Practical Limitations

"As materials scientist Pol Duwez once observed, most industrially significant solids exist in a metastable state," Chu explained. "The challenge lies in the fat that many of the most intriguing superconductors require high pressure to function, making them difficult to analyze and even more challenging to implement in real-world applications."

Deng and Chu's innovation offers a solution to this pressing issue.

The Pressure-Quench Protocol (PQP) - A Key Innovation

Deng and Chu pioneered the pressure-quench protocol (PQP), a method introduced in an October UH news release, to stabilize BST's superconducting states at ambient pressure—removing the necessity for high-pressure environments.

Significance of This Discovery

A Novel Approach to Material Phases

Why is this significant? It introduces a novel approach to preserving valuable material phases that typically require high-pressure conditions, enabling both fundamental research and practical applications.

Evidence of High-Pressure Phase Stability

"This experiment provides clear evidence that high-pressure-induced phases can be stabilized at ambient pressure through a delicate electronic transition, without altering symmetry," Chu stated. "This breakthrough opens new possibilities for preserving valuable material phases typically confined to high-pressure conditions and could aid in the quest for superconductors with higher transition temperatures."

Exploring New States of Matter

"Remarkably, this experiment unveiled a groundbreaking method for identigying new states of matter that neither naturally exist at ambient pressure nor emerge under high-pressure conditions," Deng noted. "It underscores PQP's potential as a powerful tool for mapping and expanding material phase diagrams."

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Physicists at the University of Houston have unlocked a new path to stable superconductivity at ambient pressure, paving the way for next-generation energy-efficient technologies. This revolutionary advancement could transform materials science, energy storage, and beyond.

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algebraic geometry energy efficiency data centers

Algebraic Geometry Brings Energy-Efficiency Solutions to Modern Data Centers

The relentless demand for data sharing, storage security and accessibility comes at a steep cost: power consumption. To address this, Virginia Tech mathematicians employ algebraic geometry to optimize data center inefficiencies.

Addressing the Energy Demands of Data Centers

"As individuals, we constantly generate massive amounts of data, which is dwarfed by the production levels of large corporations," remarked Gretchen Matthews, a mathematics professor and director of the Southwest Virginia node of the Commonwealth Cyber Initiative. "Without intelligent alternatives, backing up this data could require duplicating it two or three times over."

The Role of Algebraic Geometry in Reducing Energy Consumption

To reduce the energy demands of data replication, Matthews and Hiram Lopez, Assistant professor of mathematics, investigated the use of algebraic structures to fragment information and distribute it across nearby servers. If a server fails, the algorithm queries adjacent servers to restore the missing data.

Polynomials and Their Impact on Data Storage

Since the 1960s, mathematicians have recognized the potential of polynomials for storing information. Over the past decade, researchers have refined this concept, creating specialized polynomials that facilitate local recovery of missing data.

Improving Data Storage and Handling Requests

"Over the years, mathematicians have developed elegant mathematical structures that offer improved methods for data storage and handling additional requests," Matthews explained.

Research and Impact on the Data Center Industry

Their innovative method for data storage and service was highlighted in an invited review article published in IEEE BITS.

Addressing the National Increase in Data Center Demand

Mathews and Lopez's research coincides was a period of escalating electricity demand nationwide, with grid planners predicting a 38-gigawatt peak demand increase by 2028, primarily driven by the expansion of data centers, especially in Virginia.

Optimizing Data Storage and Energy Consumption

In addition to optimizing data storage in server farms, the method tackles the energy consumption linked to algorithms searching for requested information in data centers.

The Energy Cost of Locating and Retrieving Data

Lopez explained, "All of these structures are grounded in the physical realm, constrained by space and time. Energy is required to locate and retrieve information."

Preventing System Collapse from Excessive Traffic

When excessive traffic attempts to access the same information simultaneously, the system can collapse, a phenomenon often referred to as 'breaking the internet.' As viral content like selfies or videos spreads, every request to view or share the material interacts with the backup servers. Eventually, if no copies remain available, the server crashes.

Matthews and Lopez's Data Storage Optimization Approach

Matthews and Lopez's approach, incorporating an error-correcting code, optimizes data access and storage in two primary ways:

• Servers no longer need to store full duplicates of data, allowing them to free up additional storage capacity.
• In the event of a server failure or data loss, the algorithm doesn't need to search the entire network for the missing data—it only checks the neighboring servers for available copies.

Reed-Muller Codes and Data Recovery

In a later study and  publication for Designs, Codes and Cryptography, Matthew and his team highlighted that the inherent structure of Reed-Muller codes, a well-known class of error-correcting codes, facilitates the natural recovery of missing data.

The Global Impact of Algebraic Geometry in Real-World Problems

This application, Matthews stated, demonstrates the relevance and power of advanced mathematics in solving real-world problems, impacting not only the commonwealth but also our nation and the global community.

Achieving Sustainable Growth Through Enhanced Systems

Matthews stated that enhancing the existing systems and processes is key to achieving our objectives for sustainable growth.

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Energy efficiency in AI data operations with dual-IMC

Investigating Solution to the Von Neumann Bottleneck in AI Models

Introduction to the Research

AI models like ChatGPT are driven by complex algorithms and an insatiable need for data, which they interpret through machine learning. But what are the boundaries of their data-processing capacity? Led by Professor Sun Zhong, a team from Peking University is investigating solutions to the Von Neumann bottleneck, a key barrier to data-processing performance.

Dual-IMC Scheme for Enhanced Machine Learning

In their September 12, 2024 publication in Device, the research team introduced a dual-IMC (in-memory computing) scheme that enhances machine learning speed while significantly boosting the energy efficiency of conventional data operations.

Matrix-Vector Multiplication in Neural Networks

Software engineers and computer scientists utilize matrix-vector multiplication (MVM) operations when designing algorithms to power neural networks, a computational architecture resembling the structure and function of the human brain in AI models.

Understanding the Von Neumann Bottleneck

As datasets expand at an accelerated rate, computing performance frequently encounters bottlenecks due to the disparity between data transfer speeds and processing capabilities, commonly referred to as the Von Neumann Bottleneck. A traditional approach to this issue is the single in-memory computing (single-IMC) scheme, where neural network weights reside in memory, while input data (e.g., images) is provided externally.

Limitations of the Single-IMC Model

The drawback of the single-IMC model is the necessity of switching between on-chip and off-chip data transport, along with the dependence on digital-to-analog converters (DACs), which contribute to increased circuit size and elevated power demands.

Introducing the Dual-IMC Approach

To maximize the capabilities of the IMC principle, the research team introduced a dual-IMC approach, which integrates both the weights and inputs of a neural network within the memory array, enabling fully in-memory data operations.

Testing the Dual-IMC on RAM Devices

The researchers then conducted tests of the dual-IMC on resistive random-access memory (RRAM) devices, focusing on signal recovery and image processing applications.

Key Benefits of the Dual-IMC Scheme

Below are some key benefits of the dual-IMC scheme when utilized for MVM operations:

1. Enhanced efficiency results from conducting computations entirely within memory, reducing time and energy expenditures associated with off-chip dynamic random-access memory (DRAM) and on-chip static random-access memory (SRAM).
2. Computing performance is enhanced by eliminating data movement, which has historically been a limiting factor, through comprehensive in-memory operations.
3. By eliminating digital-to-analog converters (DACs) required in the single-IMC scheme, production costs are reduced, leading to savings in chip area, computing latency, and power consumption.

Conclusion: Implications for Future Computing Architecture

Given the surging demand for data processing in the contemporary digital landscape, the findings from this research may lead to significant advancements in computing architecture and artificial intelligence.

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