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Treated refinery wastewater flowing through sand, cleans itself further with pollutant-eating bacteria, finds study

pro — Tue, 10 Sep 2024 09:15:30 +0000

Treated refinery wastewater flowing through sand, cleans itself further with pollutant-eating bacteria, finds study pro Tue, 09/10/2024 - 14:45

Refinery wastewater flowing through sand produced biofilms of pollutant-eating bacteria which inturn removed the harmful compounds from the water

? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ?Representative image from Rawpexel

Refineries, which transform crude oil into useful products like gasoline and diesel, generate large amounts of wastewater. This water, which has been used for activities like steam generation and heat transfer, often contains harmful organic and inorganic pollutants, including nitrogen-containing compounds. The wastewater then undergoes several steps of treatment to remove most of these contaminants before it can be safely discharged into the environment. Scientists have been exploring alternative additional treatment steps that are both environmentally safe and economically viable.

A recent study by researchers at the Indian Institute of Technology Bombay (IIT Bombay) on biofilters, which are water filters that use microorganisms, like bacteria, to remove pollutants, has led to an interesting observation- partially treated wastewater from refineries already carry bacteria that can remove the organic contaminants from the wastewater. The researchers only had to provide a substratum- in this case, a column of pure quartz sand - onto which the bacteria could cling to get to work.

For their study, the researchers investigated the properties of sand as a biofilter. “Sand was chosen since it is commonly used in deep bed filters used for water and wastewater treatment,” remarks Prof. Suparna Mukherji, from the Department of Environmental Science and Engineering, IIT Bombay, who led this study.

The researchers designed a biofilter made of an acrylic cylinder measuring 45 cm in length and 2 cm in diameter. They filled it with pure quartz sand to a depth of 15 cm. The filtration process begins by allowing secondary treated refinery wastewater, which has undergone the removal of toxic chemicals, to flow through the biofilter at a controlled rate of 1 to 10 mL per minute. The wastewater flowing through the sand leads to the formation of a biofilm, made of several different types of bacteria enmeshed in extracellular polymeric substances secreted by the bacteria, on the grains of sand.

Schematic of sand biofiltration, photo of Sand biofilters made of acrylic, and microscopic images of the sand with and without the biofilm. (Credits: Authors of the study)

“As the water flows through the sand bed, bacteria present in the water/wastewater get adsorbed onto the sand. Subsequently, the attached bacteria replicate and secrete extracellular polymeric substances to form a biofilm on the surface of the sand grains. Bacteria grow using dissolved oxygen, organic carbon, and other nutrients from the water flowing through the sand bed,” explains Prof. Mukherji. This biofilm in turn eats away at the organic contaminants in the water. Degradation of organic compounds containing nitrogen releases inorganic nitrogen in the form of ammonium, which is further converted to nitrate. Although some removal of nitrate may have occurred, build-up of nitrate was observed after biofiltration.

The team analysed the Chemical Oxygen Demand (COD), Total Organic Carbon (TOC), and Assimilable Organic Carbon (AOC), which are measures of the various organic compounds in the water. Analysis of COD and TOC allows researchers to estimate the concentration of organic contaminants in the water. Remarkably, they observed a significant reduction in COD, TOC, and AOC after just two recirculations of the wastewater through the biofilter.

The team also used a technique called GCxGC TOF MS, which stands for Gas chromatography Time of Flight Mass spectrometry, to detect and quantify specific organic compounds in the water. “Recirculating the wastewater up to 12 times resulted in maximum reduction in COD and TOC of 62% and 55%, (by more than half) respectively. GCxGC-TOF-MS revealed that several of the identified target (harmful) compounds could not be detected in the wastewater after 12 recirculations, suggesting 100% removal,” adds Dr. Prashant Sinha, an author of the study, who was pursuing his PhD from IIT Bombay at the time of the study.

The nitrates produced by the bacteria during filtration, through conversion of other forms of nitrogen, caused a build-up of nitrates in the treated water. “The build-up of nitrates observed is not desirable. However, refineries commonly employ reverse osmosis (RO) as the final treatment step. This process can reduce the level of nitrates in the final effluent,” says Prof. Mukherji. Biofiltration can also reduce the deposition and accumulation of unwanted material on the RO membranes by reducing AOC.

The study also delved into the biofilter’s microbial community. It turned out that the predominant bacteria belonged to a group called Proteobacteria. The group is known for their ability to break down complex organic compounds like polynuclear aromatic hydrocarbons (PAHs), which are harmful to living organisms. The Proteobacteria group includes helpful bacteria like Sphingomonadales, Burkholderiales, Rhodobacterales, and Rhodospirillales, all recognized for their role in cleaning up hazardous pollutants.

Sand biofiltration method stands out for its simplicity, meaning it could be an accessible solution for many industrial plants worldwide. It could significantly reduce the environmental footprint of oil refineries. With pure quartz sand being easily available, the overall cost of building and maintaining such a biofilter at large scales remains very low, making the filtration economical. Prof. Mukherji, however, is already planning her next steps, saying, “We would like to explore this process further using other types of media and with different types of water/ wastewater”.

Article written by:? ? ? ? ? ?Dennis C. Joy
Image/ Graphic Credit: ?Lead image: Rawpexel
? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ?Inline Image: Authors of the study
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Robots Mimic Animal Homing: Scientists uncover how animals find their way back home

pro — Tue, 27 Aug 2024 08:26:19 +0000

Robots Mimic Animal Homing: Scientists uncover how animals find their way back home pro Tue, 08/27/2024 - 13:56

IIT Bombay researchers use a robot that mimics animal movements to study how homing animals efficiently return home without getting lost or being late.

? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ?Path taken by the homing robot and a magnified view of the robot.
? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ?Credit: Dr. Nitin Kumar

Many members of the animal kingdom possess an incredible ability to find their way home from unfamiliar places, a skill known as homing. Whether it's birds flying thousands of miles during migration or ants finding their way back to their colonies after foraging, homing is crucial for their survival. Humans have even harnessed this ability of the birds to train homing pigeons to deliver messages over long distances. But how do these animals always find their way home and how do they do it so efficiently? These and many other questions about this intriguing ability remain unanswered.

Researchers at the Indian Institute of Technology Bombay are using robots to unravel the mystery behind this fascinating phenomenon. “The primary goal of our research group is to understand the physics of active and living systems. We achieve this by performing experiments on centimetres-sized self-propelled programmable robots. In simple words, we model these robots to mimic the dynamics of living organisms, both at the individual and collective levels,” remarks Dr. Nitin Kumar an Assistant Professor at the Department of Physics at IIT Bombay.

Dr. Kumar’s team has now developed a robot that mimics the foraging and homing behaviour seen in animals. This robot is designed to move on its own, much like an animal finding food
(foraging), and then to use light as a guide to return home (homing). In a new study, they have used this foraging and homing robot to study the underlying principles of homing.

The foraging robot is programmed to move in a semi-random manner, similar to how animals might wander around looking for food. This type of movement is called active Brownian (AB) motion, a computer model that mimics living dynamics. The robot's direction changes frequently due to something called rotational diffusion, which introduces a certain level of randomness to its path. When the robot needs to return home, it shifts to a different mode. The researchers shine the robot with a light gradient (a gradual change in light intensity) which the robot is programmed to follow to find its way back. This mimics how some animals might use the sun or other environmental cues to navigate. “The homing motion is similar to the AB model, except the robot undergoes frequent course corrections whenever it deviates significantly from its intended homing direction, as expected in actual living organisms” explains Dr. Kumar.

For their study, the team wanted to determine the time it took for the robot to return home, with increasing amounts of deviations from its homing path. Ironically, they observed that the reorientation rate, which is the frequency at which the robot (or an animal) should adjust its direction for successful homing, originated from the degree of randomness in its path. They discovered an 'optimal reorientation rate' for a particular value of randomness beyond which the adverse effects of increased randomness are negated by more frequent reorientations, ultimately ensuring successful homing. This suggests that animals might have evolved to reorient themselves at an optimal rate to efficiently find their way home, regardless of the noise or unpredictability in their environment.

Talking about the findings, Dr. Kumar exclaims, “The observation of a finite upper limit on return times indicates that the homing motion is inherently efficient. Our results demonstrated that if animals are always aware of the direction of their home and always correct their course whenever they deviate from the intended direction, they will surely get home within a finite time.”

To back up their findings, the researchers built a theoretical model based on the concept of ‘first-passage time’. Simply put, this model helps predict how long it will take for the robot to reach home depending on its behaviour. The model was not only able to explain the robot's experimental outcomes but also captured specific features of its homing paths, like how its orientation changes over time. The model could highlight the importance of reorientation as a strategy, showing that frequent course corrections are vital for efficient navigation.

Apart from physical experiments, the team also ran computer simulations where the robot's movement mimicked animals. This virtual robot combined active Brownian motion with occasional resets to its orientation to correct its course back towards home. These simulations matched the experimental results, reinforcing the idea that randomness and reorientation work hand-in-hand to optimise homing. “When we applied this model to the trajectories of a real biological system of a flock of homing pigeons, it showed a good agreement with our
theory, validating our hypothesis of enhanced efficiency due to frequent course corrections,” adds Dr. Kumar.

By mimicking the homing behaviours of animals, scientists have taken a significant step toward understanding the underlying principles. This study not only sheds light on how animals efficiently find their way home but also paves the way for technological advancements in robotics. Real-world navigation, however, involves more than just following a simple cue—it might include responding to changing landscapes, social interactions, and other environmental factors. “In real and more complex systems, the homing cues might be more complicated than a simple uniform gradient towards home, as modelled in our experiment. In our future research, we aim to model these scenarios in our experiment by using a combination of spatiotemporal variations in light intensity and physical obstacles,” concludes Dr. Kumar about the future direction of the research.

Article written by:	Dennis C. Joy
Image/ Graphic Credit:	Prof Nitin Kumar
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Unravelling the Sands of Time: Exploring the Mesozoic Sands of Saurashtra Basin

pro — Wed, 31 Jul 2024 10:17:03 +0000

Unravelling the Sands of Time: Exploring the Mesozoic Sands of Saurashtra Basin pro Wed, 07/31/2024 - 15:47

A new study of the Saurashtra Basin dates the minerals found in the sediments, revealing the paths of ancient rivers and the geological history of the Indian subcontinent.

Map of Western India with study area highlighted.
Credits: Roy, A.B. and Jakhar, S.R., 2002. Geology of Rajasthan (Northwest India) Precambrian to recent.?Scientific Publishers.

In Western India, spanning western Gujarat and the North of Mumbai coastline, lies the Saurashtra Basin which is an expanse of 2,40,000 SqKm area spanning sea and land. Much of the landscape here remains buried in volcanic rocks called Deccan Traps, created by volcanic eruptions at the Western Ghats during the Cretaceous period, 66 million years ago. However, the sediment beneath the volcanic ash and rocks hides the extraordinary journey of the Indian subcontinent over the millenia.

A compelling study of the sediments of the Saurashtra Basin by the Indian Institute of Technology Bombay (IIT Bombay) and National Centre for Earth Science Studies, Thiruvananthapuram is helping us piece together the region’s paleogeography—a historical study that tells us what the parts of the earth looked like in the past. The study reveals some secrets of the history of India and the ancient supercontinent configurations, providing insights into how continents were formed and have moved over time.

“Saurashtra Basin was formed by the separation of India from Madagascar about 100 million years ago. Before the separation, India, Madagascar, and the Seychelles were joined together. After the separation, the western margin of India became lowlands, while the north and north-eastern parts of the study area acted as highlands,” remarks Dr. Pawan Kumar Rajak from the Department of Earth Sciences, IIT Bombay and the lead author of the study.
Rivers flowing from the highlands of the north and eastern regions of the subcontinent brought along with them the sediments from these regions, depositing them in the low-lying Saurashtra basin. “The eruption of Deccan volcanism (which happened later) covered a vast area of the Saurashtra Basin, making it difficult to study the sediments. Today, only the mountains/hills, river sections and road-cuts expose the sediments of that time,” adds Dr. Rajak.

The study focuses on the ‘Mesozoic’ era sandstones in the Saurashtra Basin. The Mesozoic era, known as the age of the dinosaurs, spans from about 252 to 66 million years ago. By examining the mineral content and employing cutting-edge dating techniques on these sandstones, the study pieces together the source and routes of the materials leading to their current location in the basin. The team used two techniques: electron probe microanalysis (EPMA) and laser ablation-inductively coupled plasma-mass spectrometry (LA-ICP-MS). Talking about the uses of these techniques, Dr. Rajak explains, “The high precision and low detection limits of LA-ICP-MS allow us to know the minerals' composition and ages on a small scale. The EPMA can determine the composition of minerals as well as the Uranium-Thorium (U-Th) age of a mineral, such as monazite, and helps identify the possible sources.”

The team focused on zircon and monazite, minerals known for preserving geological data remarkably well. “Both are rare earth elements-bearing minerals and contain a significant amount of U and Th in their crystal lattice. Decaying U or Th into lead (Pb) is used as a geological clock. So studying these minerals helps us gain information about important geological events in the past,” explains Dr. Rajak

To the northeastern part of the Saurashtra Basin is a region with 600 metres of thick sediment, called the Dhrangdara Group, from the late Jurassic-early Cretaceous period. The new study showed that the sandstones in the Dhrangdara Group primarily originated from two primary ‘Precambrian’ sources. The Precambrian is the earliest part of Earth's history, covering most of our planet's timeline. Researchers found that the Neoproterozoic rocks (about 1 billion to 540 million years ago) and Archean rocks (dating back from 4.5 billion to 2.5 billion years ago) are the primary sources of the sediments in the Saurashtra Basin.

The study also hints at revelations about paleo-drainage patterns— the ancient river systems. These findings are crucial for understanding how landscapes evolved and reshaped over time due to geological forces. Future studies could use the knowledge gained from this study to shed light on the paths of ancient river systems.

An analysis of minerals, like rutile and tourmaline, in the sandstones indicated a varied derivation from multiple geologic sources such as granites, metapelites (metamorphosed clay-rich sediments), and tourmalinites. Their chemical characteristics helped trace the rocks back to their origins in older terrains like the Aravalli and Delhi supergroup of rocks—major regional features known for their long-standing geological history. “The analysis of the
samples shows multiple sources contributing to sediments. Initially, it was thought that the Aravalli-Delhi Fold Belt (source region) was the only contributor to the sediments in the basin,” says Dr. Rajak.

Graphical representation of sediment flow into the Saurashtra basin.
Credits: Pawan Kumar Rajak

Geochemical analysis and dating give us a sneak peek into significant global events. The study of the zircon minerals suggested a predominant contribution from formations dating back to 3.5 billion to 539 million years ago. These timelines correlate with ancient supercontinent cycles such as the formation and breakup of Columbia, Rodinia, and Gondwana. These are names given to massive landmasses that once contained most or all of the Earth's continents but eventually split and drifted apart to form the continents as we see them today.

“During the supercontinents Columbia (1800 Ma) and Rodinia (1200 Ma), all the continents of the current globe were part of a single mass. The geological ages we obtained in our study indicate that the source rocks (mountains) formed during those time cycles,” explains Prof. Santanu Banerjee, a Professor at the Department of Earth Sciences at IIT Bombay.

These findings are important not just for academic curiosity but also to offer practical insights into regional geology and resources. The Saurashtra Basin, along with nearby basins like Cambay, Kutch, and Narmada, forms a part of India's western margins, which have been identified as potential sites for hydrocarbon resources. Knowing the origin of these sediments can, therefore, assist in exploration efforts and better management of these resources.

Moreover, the study also touches on larger geological phenomena, such as significant orogenies—mountain-building events—and tectonic configurations that shaped the earth’s crust. The sands from the Mesozoic era in Saurashtra capture a historical narrative of the
Bhilwara, Aravalli, and South Delhi orogenies. These events represent significant periods where the earth’s crust was dynamically altered due to tectonic activities, leading to the formation of mountains and other geological structures.

The team behind the study is looking to explore the minerals of the basin further to improve our understanding of the geological history of the region. “The next plan is to work in the same area to refine our understanding of the source areas and paleogeographic changes of that time. We must check whether the sediments were also sourced from Madagascar and Seychelles. We plan to contact ONGC to get seismic data for the study area to determine the basin configuration and trace sediments across the Arabian Sea,” shares Prof. Banerjee about future plans to continue the research.

Article written by:

Dennis C. Joy

Image/ Graphic Credit:

Lead image: Roy, A.B. and Jakhar, S.R., 2002. Geology of Rajasthan (Northwest India) Precambrian to recent. Scientific Publishers.

Inline image: PawanKumar Rajak

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Researchers from IIT Bombay harness the power of Silicon Nitride to optimize photonic technology

pro — Mon, 08 Jul 2024 12:06:12 +0000

Researchers from IIT Bombay harness the power of Silicon Nitride to optimize photonic technology pro Mon, 07/08/2024 - 17:36

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The researchers have developed a novel method of using silicon nitride to enhance the efficiency of photonic elements, promising faster, more secure, and energy-efficient technologies for communication and information processing.

Photonic technology manipulates photons (particles of light) in a similar way as electronic devices handle electrons. This emerging field promises faster, more secure and energy-efficient technologies. In a recent study, researchers from the Indian Institute of Technology Bombay (IIT Bombay) and Tata Institute of Fundamental Research (TIFR) have developed an innovative method using silicon nitride (SiN) to enhance the efficiency of photonic elements, which are the future of communication and information processing technology.

Typically, creating photonic elements involves complex fabrication and encounters certain challenges, such as poor stability and optical losses leading to low-efficiency performance. One of the main complications arises because the light source (emitters) and photonic elements consist of different materials. This poses a challenge known as poor "coupling efficiency" which means that the light from the source is not perfectly guided into the photonic element, leading to losses and degraded performance.

To address this, researchers have been exploring ways to use the same material for both the emitters and the photonic elements, a concept known as "monolithic integration". In this study, the team of researchers turned to silicon nitride (SiN), a material that has shown potential as a good single-photon emitter at room temperature. Silicon nitride has the added benefit of being compatible with current widespread semiconductor production techniques, known as CMOS technology.

Prof Anshuman Kumar Srivastava from IIT Bombay explains, “Silicon nitride stands as a pioneering material in the realm of nanophotonics, boasting well-established prowess in the construction of integrated photonics circuits. The prominence of this work stems from the innate emitters inherent within SiN.” By managing the manipulation and enhancement of these intrinsic emissions, scientists can unlock a vast array of solutions for integrated photonics applications. “This can help harness existing capabilities of SiN and pioneer novel avenues in photonics integration, promising groundbreaking advancements in optical technologies,” adds Prof Srivastava.

The latest research revolves around a SiN structure called a microring resonator, which serves as a "microcavity" where light can bounce around, effectively being trapped to stimulate its emission. This microcavity is engineered to host these so-called "whispering gallery modes" (WGMs), which are specific type of light pathways that go around the circumference of the microcavity.

“In simple terms, a whispering gallery mode is a phenomenon where sound or light waves travel around the inside of a curved surface, like the walls of a circular room or a sphere, without much loss in intensity. This creates a kind of "whispering gallery" effect, where whispers or light signals can be heard or detected from far away on the opposite side of the curve,” elaborates Mr Anuj Kumar Singh, the PhD student who co-led this work. This is similar to the case when one whispers close to one side of a curved wall, and someone far away on the other side can hear it clearly due to the way the sound waves bounce around the curve.

“In optics, light waves can travel along the curved surface, bouncing off the walls repeatedly, leading to highly confined and long-lived light paths, which can be useful in various applications like optical resonators, sensors, and lasers,” adds Mr. Kishor Kumar Mandal, the co-lead author of this work.

However, it is challenging to introduce and extract light from whispering gallery modes. The research team addressed this by creating a small notch in the microring. This notch functions as an entry point, facilitating the effective transfer of light in and out of the cavity. Using this approach, the scientists demonstrated an efficient coupling of these light emitters into the whispering gallery modes of the silicon nitride microring cavity. This breakthrough uncovered?new and effective means of extracting the trapped light, which had previously encountered significant challenges.

Schematic layout of coupled excitation and detection of cavity mode.?
Image credit: Authors of the study

Practically, this new method could mean that we may eventually be able to manufacture on-chip emitting devices for many photonic and quantum technologies without worrying about losses or instability. Like most electronic devices, this can lead to emitting devices also being integrated on a chip. The study has demonstrated the potential of silicon nitride to serve as a platform that can efficiently manipulate light on a very small scale.

Sharing insights on the research findings, Mr. Anuj Kumar Singh says that, “this work holds promise for several real-world applications in the near future like quantum computing, secure communications, and quantum sensing. While some applications may require additional research and development for practical implementation, others could be realized sooner.” Overall, the findings of this research present SiN as a key player in photonic technologies.

The manufacturing process using SiN comes with its drawbacks/errors, which can limit the performance of this novel technique. The researchers believe that improvements are possible in the material growth techniques and cavity design, leading to enhanced performance.

“Our research contributes significantly, by enabling efficient light-matter interaction, controlled quantum emission, enhanced photonic devices, simplified integration, and the potential for quantum computing in quantum photonics. These advancements pave the way for groundbreaking applications in secure communication, ultra-fast computing, and other transformative technologies that will shape the future of science and technology,” adds Mr. Kishor Kumar Mandal.

In simpler words, a high-speed, secure, and energy-efficient digital future might be closer than we think!

Article written by:	Sudhira HS
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Study reveals how new species evolve without geographic barriers

pro — Mon, 13 May 2024 11:16:20 +0000

Study reveals how new species evolve without geographic barriers pro Mon, 05/13/2024 - 16:46

Researchers at IIT Bombay discover the role of environmental resources, genes and mating in?species in the development of new species in the? same area, challenging the traditional view that?new species can develop only in distinct geographies.

Bird beaks have evolved differently based on what they feed. Credit: L. Shyamal

The evolution of life on Earth from around 3.5 billion years ago to today has shaped the planet's biodiversity. This has included the transition from organic molecules to single-cell organisms, to multicellular organisms, and finally, complex life forms (like mammals and birds). Yet, how new species come into being remains a mystery!

A new study conducted by scientists from the Indian Institute of Technology Bombay (IIT Bombay), Mumbai published in NPJ Systems Biology and Applications, has shed light on the process of speciation, meaning the formation of new species, in the absence of geographic barriers. Traditionally, it is believed that speciation largely occurs when populations of a species are isolated from each other by geographical barriers such as mountains or bodies of water. This is called allopatric speciation. However, the new IIT Bombay research suggests that speciation can happen even when populations live in the same area without geographical barriers. This mode of speciation is called sympatric speciation.“While there is ecological evidence in favour of this (sympatric) hypothesis, there is no experimental evidence. And in the absence of a laboratory model in which to study sympatric speciation, it is difficult to understand it as a process. The motivation of our work is to understand how the environment and the underlying genetics can lead to sympatric speciation and design biologically insightful experiments,” remarks Prof Supreet Saini, Professor at the Department of Chemical Engineering and DBT/Wellcome Trust (India Alliance) Fellow at IIT Bombay and the lead researcher of this study, as the motivation behind this study.

The researchers used a genetic-based model to investigate the factors that contribute to speciation when populations live in the same geographic area. This theoretical study focused on a population of birds using simulated data and specifically looked at how three aspects that encourage speciation, namely, disruptive selection, sexual selection, and genetic architecture play a role in driving and maintaining sympatric speciation.

Disruptive speciation

“In sympatric speciation, the “divide” in the population can be created due to non-uniform resources present in the environment, and geography has no role to play here. This is called ecological disruptive selection,” explains co-author Pavithra Venkataraman, PhD student & a Prime Minister’s Research Fellow at IIT Bombay. In other words, disruptive selection is a process by which individuals with extreme traits have a higher fitness than those with intermediate traits. Pavithra adds, “Disruptive selection is necessary for speciation to occur in sympatry because it (a) favours heritable differences in the population, and (b) ensures that the offsprings produced by the mating of individuals belonging to two different groups do not survive. These two factors are extremely important for maintaining biodiversity in sympatry.”

In this study, the researchers focused on a physical trait of the birds - the beak size. The birds in the population had to adapt their beak size to best utilise two types of food resources, A (say, nuts) and B (flower nectar). Birds with small beaks will be better at utilising resource A, while those with longer beaks will be more efficient at utilising resource B.

Role of sexual selection

Sexual selection, on the other hand, is a type of natural selection driven by competition for mates. It can lead to the evolution of elaborate traits that are attractive to potential mates. In this study, the researchers looked at how female mating preference, based on a male's intensity of the trait (unique character), could play a role in speciation.

“Sexual selection has been thought to be one of the main, or often, the sole driver of sympatric speciation. Essentially, it was thought that some members of the populations could evolve a ‘bias’ towards a trait like feather colour, and a difference in this bias could lead to sympatric speciation.For example, consider a bird population where there are two types of feathers - blue and red. If a bias evolves among the blue birds to only mate with their kind, sympatric speciation would occur because the red birds don’t mix their genes with the blue ones,” explains Pavithra. In other words, this could lead to populations with distinct blue and red traits.

“The drawback of this hypothesis is that there is no basis for such a bias to evolve unless there is a fitness benefit. In other words, why does a blue bird mate only with a blue one, reducing its mate pool is not clear,” questions Pavithra.

The researchers then also incorporated the ability of the bird to utilise resources in the environment into their model. Surprisingly, the researchers found that sexual selection based on special traits did not contribute to speciation in sympatry. Instead, they found that the preference for mates based on a relevant trait that helps in utilising the environmental resources better (in this case, beak size), was the driving force behind speciation. The study also acknowledges the possibility of lower fitness of the offsprings due to sexual selection.
?
Genetic architecture has a key role?

Furthermore, the researchers discovered that genetic architecture, or how genes control the trait under selection, was a crucial factor in determining the likelihood of sympatric speciation. If the genetic architecture allowed changes in beak size, then a new species could develop even with a weak role of disruptive selection.?

On the limitations of the study, Prof Saini mentioned, “In our model, we assume that birds from the two groups mate without any bias and that this bias does not change with time. This may not be true in natural populations, where a bias based on the beak size is expected to evolve. It is also possible for the birds of the two groups to evolve with distinct markers that help them distinguish their “kind” from the other.”

Nevertheless, this study provides valuable insights into the conditions and mechanisms that can lead to sympatric speciation. It challenges the traditional view that speciation can only occur in geographical isolation and highlights the importance of genetic architecture and ecological
selection in driving the formation of new species.

“A large part of our research effort is to take lessons from theory and to design experiments for understanding how reproductive barriers evolve between members of the population in sympatry. Towards this, we work with yeast to demonstrate & establish a laboratory model to study
speciation in sympatry,” adds Prof Saini while indicating the path ahead.

By unravelling the mysteries of speciation, scientists are gaining a deeper understanding of the incredible diversity of life on our planet and the processes that generate it. By demonstrating how sympatric speciation can occur, even with relatively low levels of disruptive selection, theresearchers have provided a framework for future experimental studies on biodiversity. This knowledge could open up new avenues for research and help scientists better understand the mechanisms behind the biodiversity on Earth. With the imminent threat of climate change, perhaps?this can also shed light on the impacts of climate change on biodiversity at large.

Article written by:	Sudhira H.S.
Image/ Graphic Credit:	L. Shyamala
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Mimicking locust brains: scientists develop low-power artificial neurons capable of obstacle detection

pro — Mon, 22 Apr 2024 05:08:03 +0000

Mimicking locust brains: scientists develop low-power artificial neurons capable of obstacle detection pro Mon, 04/22/2024 - 10:38

Researchers have built two-dimensional materials-based transistors and used them to design ultra-low power artificial neuron circuits for autonomous robots.

In the age of artificial intelligence (AI), there are many exciting developments from chatbots powered by large-language models to autonomous vehicles and self-driving cars. We are in a very exciting phase of continuously evolving AI, witnessing how these innovations are unfolding and impacting our lives. While Tesla’s self-driving cars have hit the market in many other countries, India’s own Pragyan rover (built by ISRO) navigated itself on the unchartered surface of the moon.

One of the key challenges in autonomous vehicles is the ability to accurately and quickly detect moving obstacles. The existing obstacle detection systems, based on complex algorithms and vision systems, are often inefficient in terms of energy consumption and size. In a recent study, researchers from the Indian Institute of Technology Bombay (IIT Bombay) and King’s College London, United Kingdom have designed and built an ultra-low power transistor, which when incorporated into their artificial neuron circuit design, is capable of obstacle detection. The circuit mimics the spiking neuron model of biological neurons.

The researchers were motivated by the brain's unique ability to process information in a distinctive manner. Particularly, they took note of the behaviour of a collision-detecting neuron found in locusts. The neuron, called lobula giant movement detector (LGMD), plays a crucial role in?helping locusts avoid collisions with objects in their path. The mechanism is similar to the way a computer works, but the brain does it in a much more energy-efficient way. In the current study, the team has designed a new type of low-power artificial neuron circuit that closely mimics the behaviour of this collision-detecting neuron found in locusts.

The novel artificial neuron circuit is designed by incorporating the models of a new subthreshold transistor built using a two-dimensional (2D) material channel. The use of ultra-thin 2D materials allows reconfigurable and low-power operation, making it suitable for energy-efficient applications. The transistor was carefully designed and fabricated to replicate sodium channel behaviour in biological neurons besides operating under a low-current regime, which enhances its energy efficiency.

Explaining the rationale behind choosing a 2D material for the transistor, Prof Saurabh Lodha, from the Department of Electrical Engineering, Indian Institute of Technology Bombay (IITB) and lead author behind the study says, ”Unlike modern computers, the human brain consumes extremely low power for memory and computing. Hence, low power consumption is a key requirement for neuromorphic (modelled after the human brain) electronics. 2D materials are ideally suited for this purpose due to their atomically-thin nature that allows for excellent electrostatic control leading to low-power operation. Although conventional semiconductors such as silicon can be thinned down as well, they lose their performance dramatically at scaled thicknesses, unlike 2D materials”.

The researchers demonstrated the low power consumption in simulations by incorporating the model of the newly built transistor into a neuronal circuit. They showed that the artificial neuron circuit closely matches the essential computational features of the LGMD neuron. It can generate LGMD-like spiking behaviour, wherein voltage spikes are produced in response to an input current signal, and detect obstacles at a low energy cost. The energy per spike of the artificial neuron is estimated to be around 3.5 picojoules (pJ), which makes it highly energy efficient compared to existing biomimetic spiking neurons.

Recollecting the challenges the researchers faced, Kartikey Thakar, the first author of the recent study notes that, “The main challenge was to achieve all essential features and spike times to match the biological LGMD neuron response. Another major challenge was the minimisation of the total energy dissipation of the entire circuit to the best-in-class among other 2D material-based reports. Careful design of the 2D subthreshold transistor characteristics played a critical role in achieving both of these results, which helped the work stand out among its peers”.

The LGMD-like neuron circuit, when provided with inputs mimicking collisions, was able to accurately detect looming objects, signalling a potential collision, at an energy cost of less than?100 pJ. Furthermore, the circuit could distinguish between looming and receding objects,?providing a selective response to approaching objects in the direct collision path. This selectivity is crucial for prioritising the system's response to potential threats. The artificial neuron also continues to function reliably even when there are variations in the current or noise in the input, making it robust and reliable for real-world applications.

The results of this research have important implications in the field of autonomous robotics and vehicle navigation. The ultra-low power spiking neuron circuit could be seamlessly integrated into existing systems, enabling accurate and energy-efficient obstacle detection. This could greatly enhance the safety and reliability of autonomous vehicles operating in unknown or dynamic environments.

Prof Bipin Rajendran, Department of Engineering, King’s College London, and a co-author of this study, says “We demonstrated that this spiking neuron circuit can be used for obstacle detection. However, the circuit can be used in other neuromorphic (systems mimicking the human brain) applications based on analog or mixed signal technology that require a low-energy spiking neuron.”

Speaking on the prospects of translating this to the market, Prof Saurabh Lodha says that, “the semiconductor industry has shown increasing interest in 2D materials for its future transistor roadmap. Their wider adoption in the industry will greatly depend on the nature of the solutions to some of the technological challenges related to 2D materials-based devices. Specifically, these solutions will have to be compatible with existing technology platforms from a process and complexity perspective, be it logic, memory or MEMS, easily integrable with other features on the semiconductor technology roadmap, e.g. heterogeneous integration, and enable future technologies such as quantum computing”.

Overall, this research represents a significant advancement in the field of neuromorphic engineering and autonomous robotics using 2D materials in the development of low-power spiking neuron circuits. The research findings can potentially revolutionise obstacle detection and avoidance and pave the way for further exploration of advanced neuromorphic systems and their integration into real-world applications.

Article written by:	Dennis C. Joy
Image/ Graphics credit:	Dennis C. Joy
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Mon, 04/22/2024 - 12:00

Strain-Resistant ‘Buckled Xenes’ Show Promise in Flexible Electronics

pro — Wed, 17 Apr 2024 11:03:56 +0000

Strain-Resistant ‘Buckled Xenes’ Show Promise in Flexible Electronics pro Wed, 04/17/2024 - 16:33

Scientists theoretically probe the atomic properties of the 2D materials under strain

Graphene, a 2-dimensional (2D) sheet made of a single layer of carbon atoms, is a wonder material with remarkable electrical, mechanical, and chemical characteristics. Its properties make it ideal for a diverse set of applications spanning various industries. The discovery soon led to the development of a host of other 2D materials, collectively called X-enes or just Xenes. Here, the X can be an element, from group IV of the periodic table such as silicon, germanium, and tin, that has 2D counterparts called silicene, germanene, and stanene, (from the Latin name for tin -stannum). Atomically, the Xenes share a lot of the characteristics of graphene, but add more features, making them candidates for use in modern digital infrastructure.

While graphene is formed by a flat sheet of carbon atoms in a single plane, the atoms of many other 2D materials arrange themselves in a zig-zag or chainsaw (As in figure) pattern earning them the name ‘buckled Xenes’. “The degree of buckling can detect the electronic character or influence the chemical reactivity. So, buckling can foster a number of potential functionalities that can be readily engineered,” explains Mr. Swastik Sahoo, a doctoral student at the Department of Electrical Engineering at the Indian Institute of Technology, Bombay (IIT Bombay).

As a new entrant into the world of material science, the properties of buckled Xenes are still being investigated. In a first, Mr. Sahoo and the team consisting of researchers from IIT Bombay and India Korea Science and Technology Center (IKST), Bengaluru, led by Prof. Bhaskaran Muralidharan, a Professor at the Department of Electrical Engineering, IIT Bombay, used theoretical analyses to predict the properties of buckled Xenes. They used well-known but complementary quantum theories - Density Functional Theory (DFT) and Quantum Transport theories to probe the material at an atomic level, to determine the electrical properties of these materials, especially when strain is applied to them. DFT is a quantum mechanical model that allows us to study the properties of many-electron systems, like atoms with multiple electrons in orbit, while quantum transport theory explains how the particles within an atom move across the device structure as a voltage is applied, thus probing its electrical properties.

According to Prof. Muralidharan, “DFT has strong predictive power, even for completely new molecules or materials. DFT calculations are used to help understand how materials and devices behave and operate under different conditions.” The DFT calculations were done in collaboration with IKST Bengaluru with the R & D Head Dr. Satadeep Bhattacharjee as the lead. The researchers used DFT to understand the behaviour of the buckled Xenes under different strain conditions. Next, quantum transport theory was employed to understand the change in the electronic properties of the material under different conditions, like increasing the application of strain. “Quantum transport theory is based on the Landauer approach, which is a simple, physical approach to analyse electron transport at nanoscale. The Landauer formula is a valuable tool for calculating current-voltage characteristics,” he adds.

Using these quantum principles, the team studied how strain applied to buckled Xenes affected the properties of the material, particularly, a property called directional piezoresistance. Piezoresistance is a property of materials wherein strain applied to the material changes the electrical resistance of the material. The study reports that 2D-Xene materials exhibit robust stability under strain, meaning they keep their electrical and mechanical performance at high-levels when bent, stretched or twisted - all important qualities for the development of flexible electronics, like wearables and smartphones.

The team focused on silicene - a single layer of silicon atoms - and compared it to other materials in the Xene family, such as germanene, stanene, and phosphorene. “Silicene is a prime contender for various applications due to its consonance with the already certified silicon industry,” remarks Mr. Sahoo. From their study, the researchers at IIT Bombay have deduced the transport angles for 2D Xenes, which is the critical angle at which the electrical properties of the 2D Xene remain stable. Stanene is especially notable, maintaining stability even when strain up to 10% is applied. Interestingly, they report that the changes corresponding to changing strain levels follow a sinusoidal pattern, a characteristic that might help engineers design smart devices, like folding smartphones and smart screens, that react predictably to twisting and bending.

Looking ahead, the scientists plan to further explore the capabilities of buckled Xenes, particularly focusing on their interactions with spintronics (a study of the effects of the intrinsic spin of electrons on semiconductors) and the effects of strain on the interface between buckled Xenes and metal substrates.

The researchers anticipate that the versatility and stability of buckled Xenes will pave the way for significant advancements in various industries. The study could open doors to a world of high-performance, flexible electronics. Buckled Xenes offer exciting new possibilities for applications such as roll-up computers, wearable technology, and advanced quantum devices, promising a future defined by unparalleled flexibility and efficiency in electronics.

Article written by:	Dennis Joy
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Walking towards a solution for early Parkinson's detection

madhuriw — Thu, 29 Feb 2024 10:09:26 +0000

Walking towards a solution for early Parkinson's detection madhuriw Thu, 02/29/2024 - 15:39

New study uses mathematical analysis of walking patterns for early detection of Parkinson’s disorder.

Parkinson's disease (PD) is a neurodegenerative disorder affecting neurons in the brain. Named after James Parkinson, who first described the condition in 1817, PD is characterised by the progressive degeneration of dopamine-producing neurons in the brain called dopaminergic neurons. Dopamine is a hormone and a neurotransmitter – a chemical that allows neurons to talk to each other. Dopamine plays a crucial role in coordinating controlled movements of the muscles, as well as regulating other functions like mood, memory, sleep, and learning. A decrease in dopaminergic neurons, and consequently in dopamine, can affect an individual’s movement as well as other functions.

An insidious disorder, early symptoms of PD are particularly hard to spot. Many individuals remain undiagnosed until symptoms become conspicuous and severe, making treatment far more challenging at this advanced stage of PD. The hallmark symptoms of Parkinson's disease include tremors, rigidity of muscles, slowness of movement, and postural instability. As the disease progresses, individuals may also experience difficulties with balance, coordination, and a range of non-motor symptoms such as sleep disturbances, mood changes, and cognitive impairment.

The latent signs of PD start intermittently, and progressively become more consistent as the disease develops over time. By the time symptoms manifest, around 50% to 80% of dopaminergic neurons have already degenerated. This makes the initial diagnosis notoriously difficult but remarkably crucial in mitigating the negative impacts later on.
Now, in an advancement in early PD detection, researchers from the Indian Institute of Technology, Bombay (IIT Bombay) and Monash University, Australia have proposed an innovative method that allows the earliest and simplest way to detect PD, even before the patients show any obvious symptoms. The study proposes analysing slight differences or inconsistencies in the walking patterns of individuals and using well-established mathematical tools to detect PD.

PD primarily manifests as a loss of control over body movements or motor functions such as walking, talking, and gripping objects. Based on this knowledge, the researchers hypothesise that there could be subtle and intermittent differences over time in the way individuals affected by PD walked. They analysed the walking pattern, known as 'gait' data, from patients using an algorithm called Dynamic Time Warping (DTW). DTW is an algorithm that allows comparison between two temporal sequences (events happening over time), like a person walking, writing or speaking. “DTW in general is an algorithm to find similarity between two temporal sequences which may vary in speed. For eg. the same person walking at different speeds,” explains Ms. Parvathy Nair, a doctoral student at the IIT Bombay-Monash Research Academy and the lead author of the study. She was guided by Prof. Maryam Shojaei Baghini, a professor at IIT Bombay, and co-advised by Prof. Hoam Chung from Monash University.

While comparing gait data using DTW, any minor, intermittent differences between any pairs of cycles of the same event, show up as a high difference compared to other pairs. “A disturbed gait is the most common, debilitating symptom of PD. Hence, we developed a generic algorithm (DTW) to detect the intermittent gait disturbances that may appear due to any early-stage symptom,” adds Ms. Nair.

Next, using a mathematical technique called K-means clustering, the data is organised into clusters, which allows for the extraction of identifiable features for each individual being tested based on the DTW data. Ms. Nair explains, “These final features are fed to a simple logistic regression (a statistical model) to easily and successfully detect early PD symptoms, which was reported as challenging using conventional statistical features”. She adds, “In our algorithm, the feature extraction using DTW and K-means is performed separately for each subject to avoid any effects or outliers due to variations in the style and speed of walking in different people”.

The researchers tested their model on a total of 166 participants, where 83 were in the early stages of PD, 10 participants were in the middle stages, and 73 were healthy individuals considered as the control group. They used gait data of the patients from three different studies which has been compiled in a database named Physionet database. They found that their model predicted PD in the test subjects at an impressive 98% prediction accuracy, where 89% of the patients were in the early stages of PD. Moreover, since the technique used is generic, the researchers believe that the method could also be used to detect other neurodegenerative disorders, where motor functions are affected.

“There are still many early motor symptoms of PD, which are not visually observable but correlate with many fine details of the human body movements. Choice of the right function and the rightly positioned sensors are the key and are a part of the ongoing research. Outputs of such wearable sensors, to be collected on a timely basis along with the repeatability pattern analysis, will open more avenues towards early detection of PD. We are continuing the research in this direction,” remarks Prof. Maryam Shojaei Baghini.

The key to combating Parkinson's lies in early detection. Though the disease is incurable, early diagnosis can assist medical practitioners in initiating the right treatment procedures, thus reducing the severity of symptoms in later stages. In addition, early treatment could potentially help in preventing the manifestation of classical motor symptoms, enhancing the patient's quality of life and substantially lowering treatment costs.

Article written by:	Dennis C. Joy
Image/ Graphic Credit:	Freepik, Nair et al. 2023, Scientifickly
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Harnessing the power of defects to improve metallic alloys

madhuriw — Tue, 06 Feb 2024 10:54:49 +0000

Harnessing the power of defects to improve metallic alloys madhuriw Tue, 02/06/2024 - 16:24

Learning about dislocations and their interactions within an alloy can help precisely engineer its properties

Two new studies from the Indian Institute of Technology Bombay (IIT Bombay), Mumbai, show the importance of defects in the arrangement of atoms in a crystal, called dislocations, in shaping the physical properties of metallic alloys.

Crystals are often imagined as perfect arrays of atoms, or molecules arranged in rows and columns. Yet, perfect crystals are rarely seen in reality. Most crystal lattices have defects, and one of the types of defects is called dislocation. A dislocation occurs when there is an irregularity or a break in the periodic arrangement of atoms or molecules in a crystal. Essentially, it is a sheet of missing atoms in the regular crystal pattern, causing the planes of the neighbouring atoms to shift to fill up the space left by the missing atoms.?

Although a defect, the presence of dislocations alters the physical properties of the material – a fact that material scientists have been exploiting to precisely engineer the physical properties of a material, such as its strength, ductility and electrical conductivity.

In alloys of iron, such as the molybdenum-containing maraging steel, molybdenum atoms are distributed throughout a matrix of iron atoms. When dislocations are present, they act like pipes through which the solute molybdenum atoms can travel much faster as compared to a dislocation-free material, in a process called pipe diffusion. The faster travel of the solute atoms through the dislocations aids in faster ageing of the alloy. Ageing, also called precipitation hardening, is a method of strengthening a material by heating it over long periods until it reaches a desired strength. During the heating, precipitates of the solute atoms (in this case, Fe₂Mo) are formed throughout the solvent material, thus strengthening the alloy. The more the number of dislocations, the faster the solute atoms could diffuse through the material, reducing the amount of time and energy required to achieve the desired level of ageing.?

In their first?study,?the team from IIT Bombay observed that the shape of the precipitates that formed by pipe diffusion had been altered by faster diffusion of the solute. Precipitates are formed by clumps of solute atoms moving through the material. They were no longer their regular spherical shape but were flattened into a plate-like structure. The team used computer models and simulations to show that as the precipitates grew around dislocation forests, their shape was also altered depending on their interaction with the dislocations. “The change in the morphology of the precipitates causes deterioration in the alloy’s properties, especially its ductility, which is not a good thing. The study gives us clues as to how to control the prior deformation to introduce just enough dislocations to gain the benefits of faster ageing while making sure too many flat precipitates are not produced” explains Prof. Nagamani Jaya, who was part of the study.?

For their subsequent?study, the team wanted to know how a single dislocation interacted with the solutes in the alloy, specifically during phase separation. Phase separation occurs when two phases separate from a single homogenous mixture. For example, when mixing oil and water, the two liquids undergo phase separation to form separate layers of water and oil. The team wanted to study how the presence of a dislocation aided or affected phase separation in metallic alloys. They once again built a computer model to represent a dislocation within a metallic alloy and ran the simulations.?

Phase separation usually happens in two ways – nucleation and growth and spinodal decomposition. Nucleation occurs when a small amount of solute atoms of the alloy accumulates at one point within the mixture. Once this accumulating mass reaches a critical size, it starts to grow. For example, considering Fe₂Mo again, molybdenum atoms will accumulate and grow from a nucleation point within the iron matrix, eventually separating the two. Dislocation networks usually provide a favourable site for the material to start the accumulation and thus start the nucleation and growth process. Spinodal decomposition, on the other hand, happens spontaneously where, at particular compositions, the two components of the alloy phase separate, akin to water and oil separating spontaneously into two distinct layers. Although both are well-known phenomena, it was thought that the two processes never occurred simultaneously in the same material.?

In their study, the team from IIT Bombay found that both nucleation and growth and spinodal decomposition could happen at the same time within the material. While a single dislocation aided spinodal decomposition, when there were two intersecting dislocations, it also helped nucleation and growth. “We observed that beyond a certain level of composition (percentages of the two metals in the alloy), spinodal decomposition can happen along the dislocation line. But when we consider a dislocation network instead of a single dislocation, at the junction where two dislocations intersect, nucleation also happens. This is the first time in literature, to the best of our knowledge, that both spinodal and nucleation are shown to occur at the same time,” says Arjun Varma R., an author of the study. The team used non-dimensionalized parameters (variables or parameters that are independent of any particular material) for their simulations, which meant the same model could be used to study different metallic alloys.??

“When we first saw spinodal and nucleation happening simultaneously, I thought there must be something wrong with our model since we are not trained to think about alloys like this. It took me some time to accept that this was, in fact, true,” exclaims Prof. M.P Gururajan, who was part of both studies. Their models were further validated by comparing atom probe results (a type of microscopy for studying atomic structures) from literature in Iron-Manganese alloy, which showed evidence of spinodal happening at the dislocations, In addition to this, they have also predicted, using this model, the range of compositions at which there is a possibility of spinodal decomposition along dislocations, for different alloys.?

Both studies show us how dislocations play an important role in determining the physical properties of alloys. It allows us to precisely engineer the dislocations to benefit from it while taming its drawbacks. Apart from applications, the studies also give us an insight into the fundamental science of the behaviour of dislocations and their interactions with the atoms of metallic alloys.?

Moreover, apart from the lessons in metallurgy, the studies also encouraged the in-house development of code and computer models, which can be used to study other metallic systems and alloys. “Phase field modelling is one of the benchmark problems for high-speed computing. It also takes a long time to run, depending on the type of supercomputer used. Having built the models and written the code ourselves, our group now has the expertise to further improve the understanding of these materials. These skills developed during these studies are one of the most important aspects for us,” remarks Prof. Gururajan.

Article written by	Dennis C. Joy
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Effect of dislocation network on precipitate morphology and deformation behavio…

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Tue, 02/06/2024 - 12:00