“Comprehensive Instructions for Attaining an Ideal Salt Ring Residue in Your Pasta Cookware”

### The Science Behind Salt Rings in Your Pasta Pot: An Intriguing Intersection of Physics and Daily Life

Have you ever observed the white salt ring that appears at the bottom of your pot after cooking pasta? Though it may seem like a trivial aspect of kitchen routines, it is actually a captivating phenomenon that has piqued the interest of physicists. A research team under physicist Mathieu Souzy from the University of Twente recently explored the science behind this situation, revealing a complex interaction of physical mechanisms that determine the creation of salt rings. Their results, published in the journal *Physics of Fluids*, indicate that elements like particle size, settling height, and particle volume significantly influence the emergence of these patterns.

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### An Intriguing Discovery Triggers Scientific Exploration

The narrative starts with a casual evening filled with pasta and board games. Souzy and his associates spotted the white salt ring left after adding salt to boiling water. This seemingly insignificant detail incited a spirited discussion and ultimately led to a comprehensive experimental protocol outlined on a whiteboard. The team embarked on a journey to investigate the mechanisms underlying the salt ring phenomenon and whether alternative deposit shapes could form.

Their inquisitiveness aligns with a wider scientific fascination with comparable phenomena, such as the “coffee ring effect.” This effect manifests when a droplet of liquid evaporates, leaving a solid ring at the edges. The mechanism is driven by quicker evaporation at the droplet’s margins, prompting a liquid—and dissolved solids—flow toward the circumference. Similar patterns have been noted in “whiskey webs,” the distinctive drying patterns found in American whiskeys, and in droplets of watercolor paint, where pigment particles move outward as the liquid evaporates.

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### The Experimental Design: Recreating Salt Rings in the Laboratory

To study the salt ring phenomenon, Souzy and his team devised a series of experiments utilizing a large transparent water tank. This configuration allowed them to examine the influence of various variables without interference from the tank’s boundaries. Instead of salt particles, they employed spherical borosilicate glass beads of different sizes to simulate particles. The beads were placed into cylindrical tubes, which were then opened at the bottom to release the particles into the water.

A Nikon D300 camera documented the motion of the beads as they descended through the water, while an LED light screen positioned beneath the tank provided consistent lighting. This thorough setup permitted the researchers to analyze how the beads settled and created formations on the tank’s base.

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### Major Discoveries: The Physics of Salt Rings

The investigation uncovered that the development of salt rings is influenced by three primary factors:

1. **Particle Size**: Larger particles tend to disperse radially outward more than smaller ones as they descend, resulting in a wider ring.
2. **Settling Height**: Particles released from a greater height take more time to settle and spread out more, leading to a uniform ring. Conversely, particles released from a lower height fall more quickly, creating a pattern with a clear central area.
3. **Injection Volume**: The quantity of particles released at once impacts the density and dimensions of the resultant ring.

As particles descend through the water, gravity pulls them downwards, producing a slight wake drag that disrupts the surrounding fluid. When multiple particles are released, their wakes interact, causing a horizontal shift and forming an expanding circular formation. Over time, the particles settle into a ring-like deposition on the tank’s bottom.

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### Wider Implications: Beyond the Culinary Realm

Though the research was sparked by a kitchen observation, its significance extends well beyond culinary applications. The researchers highlighted that similar mechanisms are at work in various geophysical and industrial activities, such as the discharge of dredged materials or industrial effluents into water bodies. Grasping how particles engage with fluids in these scenarios is essential, particularly regarding the management of contaminated waste.

The study also emphasizes the potential for size-based particle sorting. By introducing particles into a water tank, larger particles can be distinguished from smaller ones due to their varied radial movements. This understanding could have practical applications in industries requiring precise particle classification.

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### A Simple Observation, a Multifaceted Phenomenon

What started as a simple observation during a pasta dinner evolved into a profound examination of physical laws. “Despite its apparent simplicity, this phenomenon encompasses a broad spectrum of physical concepts such as sedimentation, non-creeping flow, long-range interactions between multiple bodies, and wake entrainment,” stated Souzy. The research highlights how ordinary events can conceal complex scientific principles, revealing new pathways for exploration.

Thus, the next time you witness a salt ring in your pasta pot, take a moment to appreciate the intricate physics involved. It’s a reminder that even the most commonplace elements of life can unveil extraordinary scientific revelations.

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### References

– Souzy, M., et al. (2025). “Salt ring in your pasta pan: Morphology of particle deposits in water.” *Physics of Fluids*. DOI:

Satellite Firm Opposes Miniaturization Movement, Concentrates on Constructing Bigger Satellites for Heavy-Lift Launch Vehicles

### K2 Space: Revolutionizing the Satellite Sector through Vertical Integration and Mega-Class Satellites

K2 Space, an emerging force in the satellite sector, is shaking things up with its groundbreaking methods for crafting and launching satellites. Established in California less than three years ago, the company recently deployed its inaugural demonstration spacecraft during a SpaceX Falcon 9 Transporter mission. With an ambitious goal to revolutionize the satellite landscape, K2 is harnessing vertical integration and the rising accessibility of large-capacity launch vehicles to provide high-performance satellites at a significantly reduced cost compared to traditional solutions.

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### **The Transition from Mass Limitations to Mass Availability**

K2’s co-founder and CEO, Karan Kunjur, is convinced that the satellite industry is on the brink of a major transformation. “We believe we’re transitioning from a phase of mass limitations to one of mass availability,” Kunjur remarked in an interview. This transition is fueled by innovations in launch vehicle technology, especially with the introduction of heavy-lift rockets such as SpaceX’s Starship and Blue Origin’s New Glenn, which are capable of transporting considerably larger payloads to space.

While the satellite sector has largely concentrated on miniaturization in the past decade—motivated by the demand for cost savings and quicker production times—K2 is pursuing a different strategy. Rather than shrinking sizes, the company is opting for larger designs, aiming to create high-capability satellites that maintain optimal performance.

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### **The Drawbacks of Smaller Satellites**

The shift towards smaller satellites has resulted in clear advantages, including lower costs and faster deployment times. Nevertheless, these advantages frequently compromise capability. Smaller satellites usually possess restricted power, diminished payload capacity, and abbreviated apertures, which can impair the quality of data collection and observations.

For instance, while conventional large satellites can produce up to 20 kilowatts of power, smaller counterparts often operate between just 1 to 2 kilowatts. This limitation on power restricts their capacity to accommodate advanced instruments or execute complex missions. Moreover, smaller satellites encounter dilemmas concerning balancing adequate payloads with onboard propellant, further curtailing their operational freedom.

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### **Focused on the Large Satellite Sector**

K2 is aiming to disrupt the large satellite market, which has experienced relatively minimal innovation compared to its smaller counterparts. A notable leader in this domain is Lockheed Martin, whose LM2100 satellite bus is a highly regarded platform used for significant missions such as the U.S. military’s Global Positioning Satellites. The LM2100 is a durable and dependable system, providing over 20 kilowatts of power and capable of functioning in geostationary orbit for 15 years or more.

However, the LM2100 comes with a steep price, estimated between $100 million and $150 million. A substantial portion of this expense arises from Lockheed’s dependence on external suppliers for critical components, like reaction wheels that can reach costs of up to $1 million each.

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### **Vertical Integration: The K2 Edge**

K2 is adopting a completely different tactic by prioritizing vertical integration. By designing and producing the majority of its satellite components internally, the company has realized significant cost savings. For example, K2 has engineered its own reaction wheels, bringing their price down from $500,000–$1 million to merely $35,000. As per Kunjur, around 80% of K2’s satellite manufacturing is vertically integrated.

The company’s premier offering is its “Mega-Class” satellite bus, which presents capabilities similar to the LM2100 but at a fraction of the price. The Mega-Class bus provides 20 kilowatts of power, accommodates payloads up to 1,000 kilograms, and comes with propulsion systems for orbital adjustments. Most notably, K2 intends to market this satellite bus for just $15 million—vastly lower than its competitors.

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### **Scalability and Launch Cost-Effectiveness**

K2’s Mega-Class satellites are crafted with scalability as a core feature. The bus is designed to be stackable, facilitating the simultaneous launch of multiple units. For instance, a Falcon 9 rocket can host up to 10 Mega-Class satellites, while SpaceX’s Starship has the capacity for as many as 50. This scalability not only lowers launch expenses but also paves the way for deploying extensive satellite constellations in middle-Earth orbit (MEO) and geostationary orbit (GEO).

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### **Interest from Government and Commercial Sectors**

K2’s innovative strategies have quickly garnered considerable interest from both government and commercial clients. In early 2026, the company aims to launch its first Mega-Class satellite as part of the “Gravitas” mission, backed by a $60 million contract with the U.S. Space Force. This mission will showcase the satellite’s ability to manage multiple experiments and transition from low-Earth orbit (LEO) to MEO, highlighting its versatility and operational excellence.

The reduced costs associated with K2

China Makes Progress in Narrowing the Distance with America’s Top “Reasoning” AI Models

**DeepSeek R1: A Revolutionary Advancement in Open-Source AI Reasoning Models**

On January 20, 2025, the Chinese AI laboratory DeepSeek launched its innovative **DeepSeek R1** model family, generating considerable enthusiasm within the artificial intelligence (AI) sector. Made available under the permissive **MIT license**, the DeepSeek R1 features an astonishing **671 billion parameters** in its most extensive version and asserts that it can rival OpenAI’s **o1 simulated reasoning (SR) model** across various benchmarks. This breakthrough represents a notable advance in the progression of open-source AI, providing a formidable alternative to proprietary models.

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### **What is DeepSeek R1?**

DeepSeek R1 is the newest player in the expanding domain of simulated reasoning (SR) models, designed to replicate human-like reasoning sequences when tackling problems. Unlike conventional large language models (LLMs) that produce responses immediately, SR models such as DeepSeek R1 take extra time to imitate reasoning processes, enhancing performance on challenges that necessitate logical deduction, mathematical reasoning, and scientific inquiry.

The DeepSeek R1 lineup comprises the premier **DeepSeek-R1-Zero** model along with six smaller, distilled versions varying from **1.5 billion to 70 billion parameters**. These distilled variations are fine-tuned for efficiency, allowing users to operate them on personal devices, like laptops, while the full-scale model demands more considerable computational capabilities. The smaller models were trained using data generated by the complete R1 model, utilizing open-source frameworks such as **Qwen** and **Llama**.

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### **How Does DeepSeek R1 Stand Against OpenAI’s o1?**

DeepSeek R1 has gained recognition for its capacity to contend with OpenAI’s o1 model in reasoning assessments. OpenAI launched its o1 family in September 2024, establishing a new benchmark for SR models. Nevertheless, DeepSeek asserts that R1 not only competes but surpasses o1 in several important areas, including:

– **AIME (American Invitational Mathematics Examination):** A rigorous mathematical reasoning evaluation.
– **MATH-500:** A collection of intricate word problems.
– **SWE-bench Verified:** A programming evaluation tool created to assess coding and debugging proficiency.

While these findings are encouraging, it is crucial to recognize that benchmarks can differ, and independent validation of DeepSeek’s assertions remains forthcoming.

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### **What Distinguishes DeepSeek R1?**

One of the most captivating aspects of DeepSeek R1 is its **inference-time reasoning approach**, enabling the model to emulate a sequential thought process. Independent AI researcher Simon Willison, who assessed one of the smaller models, described the encounter as both remarkable and engaging. In his blog, he pointed out that the model’s responses frequently initiate with a `…` pseudo-XML tag, unveiling its internal logic prior to delivering an answer. This level of transparency provides valuable understanding into the model’s reasoning pathways.

Moreover, the open-source character of DeepSeek R1 ensures it is accessible to researchers, developers, and enterprises. Unlike proprietary models, which are frequently confined by payment structures or restrictive licenses, DeepSeek R1 can be freely altered, explored, and implemented for commercial endeavors. This democratization of cutting-edge AI technology has the potential to accelerate innovation across diverse sectors.

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### **The Emergence of Open-Source SR Models**

DeepSeek R1 is part of a wider movement in the AI arena, where open-source models are swiftly narrowing the disparity with proprietary systems. Along with DeepSeek, other Chinese research institutions such as **Alibaba** and **Moonshot AI** have also introduced SR models that profess to rival OpenAI’s o1. This influx of open-source creativity is transforming the competitive landscape of the AI industry, broadening access to advanced capabilities.

However, DeepSeek R1’s Chinese origins bring certain limitations. For example, the cloud-based version of the model must comply with Chinese Internet regulations, which stipulate adherence to “core socialist values.” This implies that the model might refrain from generating responses on politically charged matters, including **Tiananmen Square** or **Taiwan’s autonomy**. Fortunately, these limitations do not affect locally hosted versions of the model, providing users outside China the opportunity to bypass such moderation mechanisms.

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### **Implications for the Future of AI**

The unveiling of DeepSeek R1 highlights the escalating significance of open-source AI in driving technological advancement. By rendering high-performance SR models available to a global audience, DeepSeek is enabling researchers and developers to investigate novel applications, ranging from education and healthcare to software engineering and scientific exploration.

Dean Ball, an AI researcher at George Mason University, emphasized the importance of this breakthrough, stating, “The remarkable performance of DeepSeek’s distilled models means that highly capable reasoners will continue to spread widely and be operable on local devices, far from the supervision of any overarching regulatory body.”

As open-source AI continues to