The future of robotics lies in our DNA

by Hansraj Agrawal | Jul 8, 2024 | Science

One doesn’t have to study DNA to only study genetics; Scientists are using its programmable nature to create autonomous molecular systems with robotic abilities. In a recent study, “Autonomous assembly and disassembly of gliding molecular robots regulated by a DNA-based molecular controller”, Ibuki Kawamata et al demonstrated how DNA can control molecular-level robots.

This is a significant step toward the future of bio-inspired robotics, with applications in medicine, environmental monitoring and nanotechnology. This research brings us closer to the reality of tiny robots — smaller than a human cell — carrying out complex tasks without human intervention.

Bio-inspired robotics

Living organisms exhibit remarkable autonomy, sensing and responding to their environment without external guidance. Inspired by this efficiency, researchers have been attempting to replicate such behaviour in artificial systems. Enter bio-inspired robotics, blending biology with engineering to create robots from biological molecules. These molecular robots, made from DNA and proteins, are designed to operate at the nanoscale, performing precise tasks within biological environments.

The researchers wanted to develop a system in which molecular robots could self-assemble and disassemble without external prompts. The molecular robots are a combination of a DNA-based molecular controller (specific DNA complexes and enzymes), microtubule (protein) structures and kinesin (protein) motors. These robots were programmed to autonomously form and break apart structures, mimicking natural cellular behaviours.

The molecular controller is designed to generate two different DNA strands that serve as assembly and disassembly signals for the DNA-functionalised microtubules. These DNA signals are designed to trigger specific interactions between the microtubules, leading to their assembly into bundle-like structures or disassembly into individual filaments.

The DNA controller operates through a series of strand displacement and enzymatic reactions. By carefully designing the DNA sequences and reaction cascades, the controller can autonomously perform three basic steps: signal synthesis, release of the linker, and dissociator synthesis.

Seeing is Believing

The researchers analysed the images of the performance by the fluorescent markers of the microtubules using Differential Dynamic Microscopy (DDM). This helped them understand the dynamics of the assembly and disassembly, ensuring that the system functioned as intended.

The DNA controller successfully programmed the microtubules to autonomously assemble into bundle-like structures and then disassemble into individual filaments. This autonomous behaviour was achieved without any external interference, demonstrating the controller’s effectiveness. The system maintained its autonomous function over a significant period, crucial for practical applications, ensuring that the molecular robots can perform their tasks reliably over time.

The Big Picture

The development of autonomous molecular robots is a significant leap forward in synthetic biology and robotics. These tiny machines offer unprecedented precision and control at the molecular level, opening new avenues for scientific and technological advancements.

Molecular robots can revolutionise drug delivery in healthcare and medicine. These tiny machines could be designed to deliver drugs directly to diseased cells, minimising side effects and improving treatment efficacy. By targeting specific cells, such as cancer cells, molecular robots could enhance the precision and effectiveness of treatments. Autonomous molecular robots can detect and respond to environmental pollutants and initiating clean-up processes.

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Fraunhofer’s semiconducting glass generates hydrogen from sunlight

by Hansraj Agrawal | Jul 8, 2024 | Science

While the concept of using sunlight to split water to produce hydrogen (and oxygen) without the interface of electricity (called photoelectrochemical process) is not entirely new, the German Fraunhofer Institute has come up with its own design, which uses semiconductors. Researchers from the institute have collaborated to create a modular solution that enables highly flexible hydrogen generation and supply solar energy for it.

At the heart of this technology is a tandem PEC module. It’s similar to its traditional photovoltaic counterpart, but with one crucial difference: the electricity is not generated for purposes of later electrolysis elsewhere. The entire process takes place in one unit. Caution is needed throughout — since the process results in hydrogen and oxygen, the structure must be designed to maintain a strict separation between the two elements during generation and after.

“To produce the tandem cell, experts coated standard commercially available float or plate glass with semiconducting materials on both sides,” notes a press release from the institute. When the sunlight hits the glass, one side of the module absorbs the short-wavelength light. Meanwhile, the long-wavelength light passes through the upper layer of glass and is absorbed on the reverse side. The module releases hydrogen on the reverse or cathode side and oxygen on the upper/anode side.

Over the project’s three-year term, the Fraunhofer scientists researched and developed high-purity semiconductor materials, which they apply using ultra-gentle coating methods. This allows them to increase the method’s hydrogen yield.

“We use the vapour phase to form layers that are just a few nanometres thick on the glass. The structures created in the process have a huge impact on reactor activity, in addition to the actual material properties, which we have also optimised,” explains Dr Arno Görne, group manager of Functional Materials for Hybrid Microsystems at the Fraunhofer Institute for Ceramic Technologies and Systems IKTS. The photovoltaic elements linked in the module supply the system with additional voltage — that accelerates activity and boosts efficiency.

The result is a reactor with an active surface area of half a square metre. Separated from the oxygen, it generates hydrogen, which can be captured and quantified. Right now, a single module exposed to sunlight under European conditions can generate over 30 kilograms of hydrogen per year over 100 square metres.

“In terms of the dimensions of the tandem cell, we are limited by the fact that our module splits water directly. But it is also necessary for electricity to get from one side to the other to achieve this. As the module area increases, the rising resistance has an unfavourable effect on the system. Currently, the existing format has proven to be optimal. It is stable, robust and significantly larger than any comparable solution,” Görne notes. The compact elements can be connected as needed without any negative side effects, from a single module to large areas – a significant advantage, the release says.

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Ionic latch against cancer

by Hansraj Agrawal | Jul 7, 2024 | Science

Researchers at IISER, Kolkata, have discovered how a cell surface receptor, part of a group of enzymes that interact with growth factors to control cell functions like growth, survival and movement, can help prevent cancers.

This receptor, called VEGFR1, remains inactive when it doesn’t have a ligand (a molecule that binds to it, like a hormone). Cell surface receptors, like Receptor Tyrosine Kinases (RTKs), are crucial for converting signals from outside the cell (like growth factors) into responses within the cell. When a ligand binds to these receptors, it activates enzymes inside the cell, which then add phosphate groups to other molecules, helping to regulate various cell functions such as growth and immune response. When RTKs activate on their own, without ligands, it can cause diseases like cancer, diabetes and autoimmune disorders. Researchers are studying how cells keep these enzymes inactive and what causes them to become active in diseases.

Dr Rahul Das of IISER, Kolkata, studied VEGFR, a receptor that regulates blood vessel formation, wound healing and tumour growth. They noticed that its two receptors, VEGFR1 and VEGFR2, act differently. VEGFR2 can activate on its own, but VEGFR1 cannot, even when there is a lot of it in cells. VEGFR1 binds more strongly to its ligand, VEGF-A, than VEGFR2 does, but this binding only briefly activates VEGFR1.

Activation of VEGFR1 was found to be linked to cancer-related pain and the survival and movement of cancer cells. Das has discovered a unique “ionic latch” in VEGFR1 which keeps it inactive by holding part of the receptor in place. By studying this inactive state, researchers proposed that a cell enzyme called tyrosine phosphatase plays a key role in regulating VEGFR1 activity. Their research suggests that targeting this mechanism could help treat diseases where new blood vessels form abnormally, like in cancer.

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MAB for hydrogen storage

by Hansraj Agrawal | Jul 7, 2024 | Science

Researchers at IIT-Madras have synthesised a tough, ceramic material that can conduct electricity and heat, which could possibly be used for storing hydrogen too.

A crystal structure found in certain materials called ‘MAB Phase’ gives them special properties such as high strength. In this M stands for a transition metal, like zirconium, molybdenum or titanium; A for either aluminium or silicon; and B for Boron.

Prof Somnath Bhattacharyya of the Department of Metallurgical and Materials Engineering and his team have developed a MAB phase layered ceramic — using tungsten, aluminium and boron — called WAlB. While WAlB is not a new material, known to be useful in nuclear shielding, Bhattacharyya and his team have developed a new process for making it, in a medium of molten salt. The resultant material is also of very high purity — about 98 per cent.

Bhattacharyya explained to Quantum that the material is a layered ceramic, with 2D layers of tungsten and boron, with aluminium in between. WAlB has been synthesised earlier at temperatures of 1400o C, but Bhattacharyya could do it at 800oC, at ambient pressure.

Calling the work a “breakthrough”, Dr Varun Natu, scientist at National Chemical Laboratory, Pune, observes that synthesising WAlB has traditionally proven difficult, resulting in only small crystals with low yield. However, “Bhattacharyya’s team has demonstrated not only large-scale synthesis of WAlB but also a method that uses a molten salt as a sheath. This approach significantly reduces production costs and simplifies potential future scaling, making WAlB a much more viable candidate for real-world applications.”

This material could be used as a semiconductor or for hydrogen storage, he said. Asked if the industry could take up production of this MAB at scale, Bhattacharyya said it was possible, as repeated synthesis by his students got the same, high purity material.

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Improving solar generation forecasting with ensemble of deep learning models

by Hansraj Agrawal | Jul 7, 2024 | Science

For forecasting electricity generation from fickle renewable energy sources like wind and solar, there is help coming from artificial intelligence. Machine learning and (its subset) deep learning are beginning to replace conventional, weather and satellite data based forecasting or statistical prediction models.

Deep learning models — from simple artificial neural networks to complex ‘long short-term memory’ (LSTM, which is an architecture particularly effective for making predictions based on sequential data) — are coming into play, improving the accuracy of forecasting.

Now three researchers from the Indian Institute of Engineering Science and Technology, Kolkata, Rakesh Mondal, Surajit Kumar Roy and Chandan Giri, have come up with an improved AI technique for forecasting solar generation. Instead of using a simple deep learning model, these scientists employed an ensemble of deep learning models, which they describe as “one step more advanced than simple deep learning models.” The result, they say, is higher accuracy.

The AI advantage

Not that ensemble models, which combine predictions from multiple individual deep learning models, are entirely new. In a paper published in Energy, the authors acknowledge that other researchers have tried the ensemble model method but say that they have “included features that enhance accuracy of prediction” in their own research. These features include parameters like physical characteristics of solar panels including the number of cells in a panel, the maximum working temperature of the panel, the material type and ambient temperature. “None of the existing techniques has considered these parameters for solar power prediction,” the authors say.

Mondal, Roy and Giri have used a technique called ‘Bi-directional Long Short-term Memory’ or BI-LSTM — a type of recurrent neural network (RNN) designed to handle sequential data. Unlike standard LSTM, which processes data in one direction (past to future), BI-LSTM processes data in both directions (past to future and future to past). This allows the model to have a better understanding of the context by considering both past and future information.

The researchers prepared a dataset by combining weather parameters and solar generation data and then enriched the dataset by bringing in meteorological data as well as physical characteristics of the solar panels deployed in the respective solar plants. The BI-LSTM model, they say, can predict the future solar power generation of a specific solar plant on both short and long horizons regardless of the geographical position of the solar plant.

“For short-term prediction, we can predict the generation of solar power for fifteen minutes to one hour ahead, and for long-term forecasting, we are able to predict PV power generation for 1-3 days ahead with noticeable accuracy,” the paper says.

Mondal, Roy and Giri compared the results of the proposed model with the existing dataset and multiple standard deep learning models and found that “our model produced better performance than traditional models.” They also validated their model using different solar plants in Durgapur, India. “For long-term forecasting, our model also outperformed the base model.”

From data to decisions

In an emailed response to quantum’s questions, Dr Giri said the researchers used a time series dataset containing 14 independent features and one dependent feature. The dataset contained data for every 15 minutes from January 1 to December 31, 2022. “We tested our trained model with other datasets collected from solar plants situated in Durgapur, West Bengal. Then we tested our model with a published dataset collected from Denmark. We found our model gives similar results.”

No model is flawless. “We faced some limitations during the test,” Dr Giri said, noting that when there were abrupt changes in the weather parameters, they got slightly different results.

Asked if the ensemble model would call for high computational power, Dr Giri said that their model “is quite light weight” containing only 1.2 million parameters. “We believe that it will not be an issue during large-scale implementation,” he said.

“We believe that our model is trained with a very small amount of data,” he said, adding that they were trying to extend our work with a large amount of data to improve the efficiency of our model.

This work will help the researchers explore the other dimensions rather than a specific dataset but also the scientific knowledge of specific domains, Dr Giri told quantum.

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Incipient metals

by Hansraj Agrawal | Jun 23, 2024 | Science

A study conducted by the Jawaharlal Nehru Centre for Advanced Scientific Research, Bengaluru, has unravelled the electronic mechanisms governing chemical bonding of a new class of materials called ‘incipient metals’, which can boost energy harvesting and power generation.

Sourcing new materials with unique properties can help in the advancement of current technology. Recently, scientists are turning to a class of compounds called group IV chalcogenides that have intriguing properties, making them suitable candidates for technological applications. These compounds contain an element from group VI of the periodic table combined with an element from group III–V of the periodic table, like PbTe, SnTe and GeTe, says a press release from the Department of Science and Technology, Government of India.

Chalcogenides can transition reversibly between amorphous and crystalline phases in response to changes in temperature, pressure, or electrical fields. This unique characteristic has practical applications in rewritable optical discs and electronic memory devices due to the contrasting optical responses of the two phases. Additionally, these chalcogenides are valuable in energy harvesting and power generation applications, thanks to their high electrical conductivity and effective conversion of thermal energy into electrical energy through the thermoelectric effect.

The study, by Professor Umesh Waghmare from the Theoretical Sciences Unit, explored the possibility of introducing the recently introduced metavalent bonding (MVB) within a single 2D layer of Group IV chalcogenides, investigating its mechanisms and the resulting consequences on material properties.

The theoretical work conducted by Prof Waghmare and his team has significant implications and promising applications across industries, the release says. The chalcogenides explored in this study are already employed in computer flash memories, utilising their ability to change optical properties during the transition from crystalline to amorphous states. Additionally, the potential use of these materials in energy storage, especially as phase change materials, opens avenues for more sustainable and efficient energy solutions.

Furthermore, the research connects with the emerging field of quantum materials, aligning with the goals of India’s national mission on quantum technology. These materials, with their distinct electronic structures and properties, offer a prototypical example of quantum topological materials, a critical component in advancing quantum technologies.

High entropy alloys

The reason stainless steel is useful is because the chromium in it forms an oxide layer that protects it from rusting. Today, scientists the world over are toying with multiple-metal alloys — more than five as opposed to two or three in conventional alloys — to make materials of desired properties. Under this ‘High Entropy Alloys’ are of interest because of their extreme sturdiness, which comes from the way the atoms are arranged in the alloys. One difficulty with developing HEAs is that you have to make them and test them.

Now a group of researchers at the Pacific Northwest National University has developed a tool to predict how HEAs will behave under high temperature oxidative environments. The tool helps investigate the arrangement of atoms within samples, using in situ atom probe tomography. This will fast-track development of complex alloys with exceptional high-temperature properties.

The group of researchers predominantly consists of people of Indian origin — Arun Devaraj, Bharat Gwalani, Anil Krishna Battu, Thevuthasan Suntharampillai and Aniruddha Malakar. “This work sheds light on the mechanisms of oxidation in complex alloys at the atomic scale,” says Bharat Gwalani, co-corresponding author of the study.

“Right now there are no universally applicable governing models to extrapolate how a given complex, multi-principal element alloy will oxidize and degrade over time in a high-temperature oxidation environment,” says Devaraj. The ultimate goal is to choose a combination of elements that favour the formation of an adherent oxide, he said. “You know oxide formation will happen, but you want to have a very stable oxide that will be protective, that would not change over time, and would withstand extreme heat inside a rocket engine or nuclear reactor.”

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