Cost effectivities analysis of perovskite solar cells: Will it outperform crystalline silicon ones?

Shanghai Jiao Tong University Journal Center

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• Current manufacturing cost of perovskite solar modules is calculated as 0.57 $ W−1 much higher than that of the silicon solar cells.
• Cost Effectivities analysis indicates that materials cost shares 70% of costs, and capital cost and other cost share nearly 15%, respectively.
• The cost of perovskite solar modules has the potential to outperform crystalline silicon under conditions of 25% efficiency, lifetime of 25 years, and cost reduction of materials and equipment, etc.

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Credit: Yingming Liu, Ziyang Zhang, Tianhao Wu, Wenxiang Xiang, Zhenzhen Qin, Xiangqian Shen, Yong Peng, Wenzhong Shen, Yongfang Li, Liyuan Han.

The commercialization of perovskite solar cells (PSCs) has garnered worldwide attention as the next generation of photovoltaic (PV) technology. With their high efficiency and potentially low manufacturing cost, PSCs are expected to revolutionize the renewable energy market. However, the current manufacturing cost of perovskite solar modules is calculated as 0.57 $ W-1, much higher than that of silicon solar cells. Now, Professor Liyuan Han from Shanghai Jiao Tong University, along with researchers from Wuhan University of Technology and other institutions, present a comprehensive cost-effectiveness analysis of PSCs, offering valuable insights into their potential to outperform crystalline silicon solar cells.

Why Cost Effectiveness Matters

• Current Cost Analysis : The manufacturing cost and levelized cost of electricity (LCOE) of PSCs are estimated as 0.57 $ W-1 and 18–22 US cents (kWh) -1, respectively. Materials cost accounts for 70% of the total cost, while capital cost and other costs each account for nearly 15%.
• Potential for Cost Reduction : Sensitivity analysis indicates that improving efficiency, yield, and reducing materials cost can significantly lower the cost of PSC modules. Under conditions of 25% efficiency, 25-year lifetime, and cost reduction of materials and equipment, PSCs have the potential to outperform crystalline silicon solar cells.
• Long-Term Cost Targets : To achieve cost parity with crystalline silicon modules, it is essential to refine the fabrication processes, develop stable inorganic transport materials, and precisely control material formation and processing at the microscale and nanoscale to enhance charge transport.

Innovative Strategies for Cost Reduction

• Improving Efficiency and Yield : Enhancing the efficiency and yield of PSC modules is crucial for cost reduction. For example, improving the yield from 50% to 90% can significantly reduce the cost from 0.571 to 0.324 $ W-1. However, even with high efficiency and yield, the cost of PSCs remains higher than that of crystalline silicon modules.
• Reducing Materials Cost : The cost of materials, especially FTO glass, ITO, and C60, accounts for a large proportion of the total cost. Developing new transparent conductive substrates with lower costs and using cheap inorganic electron transport materials, such as SnO2 and TiO2, are effective ways to reduce materials cost.
• Optimizing Equipment Investment : As manufacturing capacity expands, equipment investment per 100 MW can be reduced. However, the impact of equipment investment on cost reduction is relatively small, as capital cost only accounts for a small proportion of the total cost.

Future Outlook

• Short-Term Target (Scenario 1) : Achieving 20% efficiency and 90% yield within 4–5 years through the extension of current technologies is possible. However, the module cost (0.24 $ W-1) is still higher than that of crystalline silicon modules, so new markets such as mobile electronic devices, toys, see-through devices, and indoor applications should be sought for profitability.
• Long-Term Target (Scenario 2) : PSCs can obtain a cost similar to that of crystalline silicon modules under the coordination of over 25% efficiency, 99.5% yield, 40% materials cost reduction, 50% equipment investment reduction, and 30% electricity cost reduction. Achieving a lifetime of 25 years is also essential for PSCs to have a competitive LCOE.
• Research Directions : To reduce the LCOE of PSCs, several research issues need to be addressed, such as improving production yield, enhancing module efficiency and stability, developing cheap electron transport layer (ETL) and transparent conductive oxide (TCO) materials, reducing vacuum processes, and optimizing the structure of modules.

Perovskite solar cells have shown great potential to outperform crystalline silicon solar cells in terms of cost-effectiveness. However, significant efforts are still needed to improve efficiency, yield, and stability, as well as to reduce materials and equipment costs. Stay tuned for more exciting breakthroughs in the commercialization of perovskite solar cells!

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Journal

Nano-Micro Letters

DOI

10.1007/s40820-025-01744-x 

Method of Research

Experimental study

Article Title

Cost Effectivities Analysis of Perovskite Solar Cells: Will it Outperform Crystalline Silicon Ones?

 

Leg amputation caused by arterial disease four times higher in disadvantaged areas

University of Sheffield

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Leg amputation caused by arterial disease four times higher in disadvantaged areas

• A new study from the University of Sheffield highlights the scale of socioeconomic disparities in leg amputation caused by arterial disease across England

• Lower limb amputation rates are four times as high in the most socioeconomically disadvantaged areas in England compared with the least disadvantaged areas

• Peripheral arterial disease is a debilitating condition which is caused by a build-up of fatty deposits in the arteries to the legs which restricts blood supply

• Findings show patients living in the most disadvantaged areas are also more likely to die following amputation

 

Leg amputation rates caused by arterial disease are four times as high in the most disadvantaged areas in England. 

The new study, from the University of Sheffield, also found patients living in the most socioeconomically disadvantaged areas are more likely to die following leg amputation compared with those living in the least disadvantaged areas. 

Whilst socioeconomic disadvantage is associated with a higher risk of amputation, this new study highlights the worrying scale of the disparities. 

Peripheral arterial disease is a debilitating condition caused by a build-up of fatty deposits in the arteries to the legs which restricts blood supply. Major amputation of the lower limb is a last resort when other treatment options do not exist or have failed.

The main reasons for amputation include severe infection of the leg and gangrene caused by restricted arterial blood supply.

The new study, funded by the National Institute for Health and Care Research (NIHR), examined associations between socioeconomic deprivation and ethnicity and major leg amputation in England.

The researchers analysed hospital admissions and census area data over a 12-year period from 2006 to 2018. During the study period there were a total of 47,249 major lower limb amputations due to peripheral arterial disease.

The researchers found the main disparities were as a result of socioeconomic inequalities. 

Above knee amputation was around four times as high in the most disadvantaged compared with the least disadvantaged areas, while below knee amputation was around three times as high.

Although amputation rates decreased in all socioeconomic categories from 2006 to 2018 in the population aged over 65 years, there was little change in the population under 65.

Professor Ravi Maheswaran, Emeritus Professor of Epidemiology and Public Health at the University of Sheffield’s School of Medicine and Population Health, said: “Being told you need to have a leg amputated can be a devastating and frightening experience for patients.

“Greater efforts are needed in socioeconomically disadvantaged areas in order to prevent and manage peripheral arterial disease, reduce amputation rates and improve survival following amputation.”

To view the full study, please visit: https://academic.oup.com/bjsopen/article/9/3/zraf046/8155622?utm_source=advanceaccess&utm_campaign=bjsopen&utm_medium=email 

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Journal

BJS Open

DOI

10.1093/bjsopen/zraf046 

Method of Research

Observational study

Subject of Research

People

Article Title

Socioeconomic and ethnic disparities in major lower limb amputation related to peripheral arterial disease in England

Article Publication Date

8-Jun-2025

 

Have female earwigs evolved their forceps as weapons in battles for mates?

Female earwigs may use their forceps as weapons too—just like males

Toho University

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Male (left) and female (right) maritime earwigs (Anisolabis maritima).

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Credit: Junji Konuma

A new study from Toho University reveals that female earwigs exhibit a similar pattern of exaggerated forceps growth as males, suggesting that both sexes may have evolved these traits through sexual selection.

Do larger male elk have proportionally larger antlers? The answer is no. In fact, larger individuals tend to have disproportionately larger antlers—a phenomenon known as positive allometry. This pattern, where certain body parts grow disproportionately large relative to body size, is observed not only in mammals but also in animals such as beetles and fiddler crabs. Evolutionary biologists interpret such traits as evidence of sexual selection—a process in which physical features evolve because they offer an advantage in competing for mates.

Male earwigs are known to show positive allometry in their forceps—pincer-like appendages at the tip of the abdomen—which are believed to have evolved as weapons in battles with rivals. But what about females? Female earwigs also have forceps—so what purpose do they serve?

Tomoki Matsuzawa (then an undergraduate) and Associate Professor Junji Konuma from Toho University’s Department of Biology conducted the first quantitative study of female earwig forceps. Using morphometric analysis on the maritime earwigs Anisolabis maritima, they found that female forceps also display positive allometry—suggesting that they, too, may have evolved through sexual selection.

The team measured the head, thorax, abdomen, and bilateral forceps dimensions and analyzed shape differences in both sexes. They found that males have thick, short, and curved forceps, while females have thin, long, and straight ones—indicating clear sexual dimorphism. When they plotted body size against forceps width and length on a log–log scale, the results revealed a pattern of positive allometry in males: forceps width increased disproportionately with body size. Surprisingly, positive allometry was also found in females—in the length of the forceps. These results suggest that while the sexes differ in forceps shape, both may have evolved them as weapons—albeit in different ways.

Associate Professor Konuma explains:“A previous behavioral study has shown that female earwigs compete for small, non-aggressive males. Our findings suggest that female forceps may have evolved as effective weapons in such competition. While most earlier research focused only on males, our study highlights the importance of considering female traits as well when studying the evolution of insect morphologies.”

These findings were published on June 12, 2025, in the Biological Journal of the Linnean Society.

 

Konuma Lab Website
https://maimaikaburi.com/EN

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Journal

Biological Journal of the Linnean Society

DOI

10.1093/biolinnean/blaf031 

Method of Research

Observational study

Subject of Research

Animals

Article Title

Positive allometry in the forceps of the female earwig Anisolabis maritima (Dermaptera: Anisolabididae)

Article Publication Date

12-Jun-2025

 

Swarm intelligence directs longhorn crazy ants to clear the road ahead for sisters carrying bulky food

Scientists show how collective form of understanding emerges from simple actions of unintelligent worker ants

Frontiers

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Examples of experimental set-up and close-up of collective transport of prey and of obstacle-clearing behavior

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Credit: E Fonio, D Mersch, O Feinerman

Among the tens of thousands of ant species, incredible ‘intelligent’ behaviors like crop culture, animal husbandry, surgery, ‘piracy’, social distancing, and complex architecture have evolved. Yet at first sight, the brain of an ant seems hardly capable of such feats: it is about the size of a poppy seed, with only 0.25m to 1m neurons, compared to 86bn for humans. Now, researchers from Israel and Switzerland have shown how ‘swarm intelligence’ resembling advance planning can nevertheless emerge from the concerted operation of many of these tiny brains. The results are published in Frontiers in Behavioral Neuroscience.

“Here we show for the first time that workers of the longhorn crazy ant can clear obstacles from a path before they become a problem – anticipating where a large food item will need to go and preparing the way in advance. This is the first documented case of ants showing such forward-looking behavior during cooperative transport,” said Dr Ehud Fonio, a research fellow at the Weizmann Institute of Science in Israel, and the corresponding author of the study.

‘I can see all obstacles in my way’

The researchers were inspired when they made a fascinating chance observation in nature: individual crazy ant workers used their mandibles to pick up and carry away tiny gravel pebbles near groups of workers cooperating to transport large insect prey.

“When we first saw ants clearing small obstacles ahead of the moving load we were in awe. It appeared as if these tiny creatures understand the difficulties that lie ahead and try to help their friends in advance,” said Dr Ofer Feinerman, a professor at the Weizmann Institute, and the study’s final author.

Fonio et al. designed a suite of 83 experiments to study this obstacle-clearing behavior on a single crazy ant ‘supercolony’ on the Weizmann Institute’s campus. For pebbles, they used plastic beads with a diameter of 1.5 millimeter (half the body length of the ants) to block the ants’ route. For prey, they used pellets of cat food, of which the ants are fond.

Triggered into clearing mode by pheromones

Like many ant species, crazy ants are known to alert their sisters to the presence of large food items by laying odor trails: running erratically (hence their ‘crazy’ name), they touch the ground with the tip of their abdomen every 0.2 seconds to deposit a tiny droplet of a pheromone. This pheromone swiftly attracts other workers to the food. But here, the scientists found this pheromone to play a key role in clearing behavior as well.

Their observations showed that workers were most prone to clear beads that lay approximately 40mm away from food towards the direction of the nest. They moved these beads for up to 50mm before dropping them, away from the route leading back to the nest. The record holder cleared 64 beads in succession.

Such clearing behavior always occurred when the pellet was whole, but rarely when it was divided into crumbs. This distinction seemed adaptive, as the observations showed that crumbs were always carried home by single workers, who would simply walk around any beads in their path. Intact pellets, however, always prompted ‘cooperative’ transport by multiple workers, who typically remained stalled by a grid of beads until these were cleared.

That the beads were a real hindrance was also clear from the time that cooperative transport took to pass through a 5cm by 7cm tunnel: this was 18 times longer when the passage was filled with beads than when it was free of obstacles.

Further observations also revealed that workers didn’t need to be in contact with the food to start clearing behavior: they were prompted to do so by pheromones deposited by foragers. A single mark that happened to be near a bead was sufficient to put a worker in ‘clearing mode’, after which they would actively look for more beads to clear.

‘Awe-inspiring’

“Taken together, these results imply that our initial impression was wrong: in reality, individual workers don’t understand the situation at all. This intelligent behavior happens at the level of the colony, not the individual. Each ant follows simple cues – like fresh scent marks left by others – without needing to understand the bigger picture, yet together they create a smart, goal-directed outcome,” concluded Dr Danielle Mersch, formerly a postdoctoral researcher at the same institute.

“We find this to be even more awe-inspiring than our initial guess,” said Feinerman.

“Humans think ahead by imagining future events in their minds; ants don’t do that. But by interacting through chemical signals and shared actions, ant colonies can behave in surprisingly smart ways – achieving tasks that look planned, even though no single ant is doing the planning. These ants thus provide us an analogy to brains, where from the activity of the relatively simple computational units, namely neurons, some high cognition capabilities miraculously emerge.”

Worker of longhorn crazy ant clearing a bead

Credit

Alessandro Crespi

Collective transport [VIDEO] | 

Pheromone trail [VIDEO] | 

Clearing behavior [VIDEO] | 

Clearing behavior [VIDEO] | 

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Journal

Frontiers in Behavioral Neuroscience

DOI

10.3389/fnbeh.2025.1533372 

Method of Research

Experimental study

Subject of Research

Animals

Article Title

Ants engaged in cooperative food transport show anticipatory and nest-oriented clearing of the obstacles surrounding the food: goal-directed behavior emerging from collective cognition

Article Publication Date

13-Jun-2025

AI-enabled control system helps autonomous drones stay on target in uncertain environments

The system automatically learns to adapt to unknown disturbances such as gusting winds

Massachusetts Institute of Technology

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Cambridge, MA – An autonomous drone carrying water to help extinguish a wildfire in the Sierra Nevada might encounter swirling Santa Ana winds that threaten to push it off course. Rapidly adapting to these unknown disturbances inflight presents an enormous challenge for the drone’s flight control system.

To help such a drone stay on target, MIT researchers developed a new, machine learning-based adaptive control algorithm that could minimize its deviation from its intended trajectory in the face of unpredictable forces like gusty winds.

Unlike standard approaches, the new technique does not require the person programming the autonomous drone to know anything in advance about the structure of these uncertain disturbances. Instead, the control system’s artificial intelligence model learns all it needs to know from a small amount of observational data collected from 15 minutes of flight time.

Importantly, the technique automatically determines which optimization algorithm it should use to adapt to the disturbances, which improves tracking performance. It chooses the algorithm that best suits the geometry of specific disturbances this drone is facing.

The researchers train their control system to do both things simultaneously using a technique called meta-learning, which teaches the system how to adapt to different types of disturbances.

Taken together, these ingredients enable their adaptive control system to achieve 50 percent less trajectory tracking error than baseline methods in simulations and perform better with new wind speeds it didn’t see during training.

In the future, this adaptive control system could help autonomous drones more efficiently deliver heavy parcels despite strong winds or monitor fire-prone areas of a national park.

“The concurrent learning of these components is what gives our method its strength. By leveraging meta-learning, our controller can automatically make choices that will be best for quick adaptation,” says Navid Azizan, who is the Esther and Harold E. Edgerton Assistant Professor in the MIT Department of Mechanical Engineering and the Institute for Data, Systems, and Society (IDSS), a principal investigator of the Laboratory for Information and Decision Systems (LIDS), and the senior author of a paper on this control system.

Azizan is joined on the paper by lead author Sunbochen Tang, a graduate student in the Department of Aeronautics and Astronautics, and Haoyuan Sun, a graduate student in the Department of Electrical Engineering and Computer Science. The research was recently presented at the Learning for Dynamics and Control Conference.

 

Finding the right algorithm

Typically, a control system incorporates a function that models the drone and its environment, and includes some existing information on the structure of potential disturbances. But in a real world filled with uncertain conditions, it is often impossible to hand-design this structure in advance.

Many control systems use an adaptation method based on a popular optimization algorithm, known as gradient descent, to estimate the unknown parts of the problem and determine how to keep the drone as close as possible to its target trajectory during flight. However, gradient descent is only one algorithm in a larger family of algorithms available to choose, known as mirror descent.

“Mirror descent is a general family of algorithms, and for any given problem, one of these algorithms can be more suitable than others. The name of the game is how to choose the particular algorithm that is right for your problem. In our method, we automate this choice,” Azizan says.

In their control system, the researchers replaced the function that contains some structure of potential disturbances with a neural network model that learns to approximate them from data. In this way, they don’t need to have an a priori structure of the wind speeds this drone could encounter in advance.

Their method also uses an algorithm to automatically select the right mirror-descent function while learning the neural network model from data, rather than assuming a user has the ideal function picked out already. The researchers give this algorithm a range of functions to pick from, and it finds the one that best fits the problem at hand.

“Choosing a good distance-generating function to construct the right mirror-descent adaptation matters a lot in getting the right algorithm to reduce the tracking error,” Tang adds.

 

Learning to adapt

While the wind speeds the drone may encounter could change every time it takes flight, the controller’s neural network and mirror function should stay the same so they don’t need to be recomputed each time.

To make their controller more flexible, the researchers use meta-learning, teaching it to adapt by showing it a range of wind speed families during training.

“Our method can cope with different objectives because, using meta-learning, we can learn a shared representation through different scenarios efficiently from data,” Tang explains.

In the end, the user feeds the control system a target trajectory and it continuously recalculates, in real-time, how the drone should produce thrust to keep it as close as possible to that trajectory while accommodating the uncertain disturbance it encounters.

In both simulations and real-world experiments, the researchers showed that their method led to significantly less trajectory tracking error than baseline approaches with every wind speed they tested.

“Even if the wind disturbances are much stronger than we had seen during training, our technique shows that it can still handle them successfully,” Azizan adds.

In addition, the margin by which their method outperformed the baselines grew as the wind speeds intensified, showing that it can adapt to challenging environments.

The team is now performing hardware experiments to test their control system on real drones with varying wind conditions and other disturbances.

They also want to extend their method so it can handle disturbances from multiple sources at once. For instance, changing wind speeds could cause the weight of a parcel the drone is carrying to shift in flight, especially when the drone is carrying sloshing payloads.

They also want to explore continual learning, so the drone could adapt to new disturbances without the need to also be retrained on the data it has seen so far.

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This research was supported, in part, by MathWorks, the MIT-IBM Watson AI Lab, the MIT-Amazon Science Hub, and the MIT-Google Program for Computing Innovation.

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DOI

10.48550/arXiv.2407.20165