The elephant’s trunk is one of the animal world’s most formidable facial appendages; it is capable of tearing a tree limb from its base and, moments later, carefully lifting a single blade of grass.

Now, this combination of strength and precision is also helping to drive robotics research.

A boneless appendage that can both move heavy logs and peel fruit like a banana, the elephant’s trunk has long been studied by engineers seeking to understand how it can perform both powerful and delicate tasks effectively. Now, in a new study published in PNAS Nexus, researchers have identified the skin, rather than the underlying muscle, as a key factor in the versatility of this pachyderm appendage. 

Lucia Beccai and colleagues at the Istituto Italiano di Tecnologia studied the trunk of an adult Asian elephant that died of natural causes at Zurich Zoo in 2020. They collected 35 samples from different regions of the trunk and analyzed them using biomechanical testing, histology, various imaging techniques, and computational modeling. The team’s analysis revealed a trunk that combines two very different mechanical strategies within a single structure.

One Trunk, Two Skins

The elephant trunk acts as a muscular hydrostat, similar to an octopus tentacle or the human tongue. With no bones and over 100,000 muscles, the trunk can stretch, compress, twist, and bend in any direction. The trunk achieves this flexibility through a combination of muscle power and specialized skin architecture. The study found that the trunk’s outer surface varies in structure depending on location and the types of forces it encounters.

The researchers identified clear differences between the upper and lower surfaces of the trunk, reflecting what scientists call functional anisotropy. The trunk’s upper surface serves as a tough, protective layer, which is over three times stiffer than the underside. In contrast, the trunk’s lower surface remains soft and flexible, allowing the elephant to grip objects with delicacy and precision.

The way elephants use their trunks reflects this structural difference. The upper surface faces the environment and absorbs impacts and abrasions. The underside is used for gripping objects. Its lower stiffness allows it to adapt to the shape of objects and hold them securely. The design of the elephant trunk may offer a solution to a persistent challenge that has stumped soft-robotics engineers in balancing protection and precision by assigning each function to a different layer of skin.

A Lens Made of Tissue

Another significant finding was made beneath the gripping surface. The researchers identified dome-shaped structures known as dermal papillae. Finite element modeling suggested that these structures act as subsurface mechanical lenses, concentrating mechanical stress in regions where sensory nerves are located.

This design could solve an additional problem in robotics: touch sensors are often fragile and require protective coatings that can reduce their sensitivity. The tissue in the elephant trunk appears to protect sensory structures while still allowing mechanical signals to reach them.

The Biomimetic Blueprint

For Beccai’s group, which specializes in soft robotics, the possible design template represents a huge opportunity for their field. A robotic gripping device built on this idea would feature a stiff outer shell over a flexible inner layer, with structures underneath to direct pressure to protected sensors. This setup would keep sensitive electronics shielded while still allowing for mechanical feedback from touch.

This research adds to the growing scientific interest in the elephant trunk as a model for engineering. Another 2024 study in Communications Biology found that the trunk’s dermis is reinforced by tangled collagen fibers, creating skin armor thicker than an armadillo’s shell. Together, these studies show a structure optimized at every level, from the protein fibers in the dermis to the clear split between the upper and lower surfaces.

Today’s robotic hands still struggle to balance durability with sensitivity. The elephant trunk offers biological inspiration for this challenge, providing a model for future engineering designs.

Austin Burgess is a writer and researcher with a background in sales, marketing, and data analytics. He holds an MBA, a Bachelor of Science in Business Administration, and a data analytics certification. His work focuses on breaking scientific developments, with an emphasis on emerging biology, cognitive neuroscience, and archaeological discoveries.

In a groundbreaking advancement that bridges the gap between biological intelligence and artificial systems, researchers at Oregon State University have engineered a light-sensitive device that not only detects visual stimuli but also bio-mimics the brain’s ability to store and modulate memories dynamically. Drawing direct inspiration from the complex workings of human neural processes, this pioneering approach integrates sensing, memory, and signal processing into a single phototransistor, a feat poised to revolutionize the future of neuromorphic computing.

Traditional artificial intelligence hardware architectures typically bifurcate these essential functions—detection, memory retention, and processing—forcing data to shuttle between distinct components. This separation not only introduces latency but significantly amplifies energy consumption. The novel phototransistor developed by the OSU team disrupts this paradigm by embedding memory capabilities precisely where sensory inputs occur, vastly improving computational efficiency and speed.

Central to this device’s innovation is its ability to mimic the neurochemical processes governing memory strength and decay in the human brain. Rather than storing information with fixed permanence, the phototransistor utilizes trapped electrical charges generated by incident light to represent memories, which can then be pharmacologically tuned—via electrical gate voltages—to either reinforce or weaken these stored traces over time. This dynamic modulation reproduces the adaptive nature of biological synapses and opens unprecedented avenues for creating AI systems that can ‘forget’ as well as ‘remember,’ essential for real-time learning and adaptation.

Structurally, the device marries an oxide semiconductor acting as the electronic transistor channel with an organic photosensitive layer responsible for absorbing photons and generating charge carriers. The organic layer traps a subset of these carriers, creating a persistent local electric field even after illumination ceases. This trapped charge influences electron flow through the semiconductor channel, effectively encoding a memory of prior light exposure.

A key technical breakthrough lies in the device’s gate-tunable interface: by adjusting the voltage applied to the transistor’s gate terminal, researchers can manipulate the spatial positioning of trapped charges relative to the conduction channel at a nanoscopic level. This precise control modulates the strength of electrical interaction, allowing for programmable decay times of the optoelectronic memory—ranging from seconds to considerably longer durations. Such flexibility is vital for neuromorphic systems requiring adjustable time constants for processing temporal patterns.

The implications of this technology extend beyond simple visual sensing. By localizing both sensing and adaptive memory functions, the phototransistor can serve as a foundational building block for advanced vision systems capable of real-time data processing with exceptional energy efficiency. This novel hardware approach offers substantial improvements over conventional sensors, which often offload processing to centralized units, incurring latency and energy penalties.

Neuromorphic computing, a field striving to emulate the architecture and operational principles of the brain, stands to gain significantly from this development. The device’s capacity to embody synapse-like plasticity within a phototransistor embodies a leap toward systems that do not merely compute but adapt, learn, and optimize autonomously. Furthermore, the integration of these functionalities at the hardware level paves the way for compact, scalable AI platforms potentially transformative for robotics, autonomous vehicles, and sensory-rich IoT devices.

The research team acknowledges that the trapped charges’ mobility within the device’s photosensitive layer marks a fundamental departure from fixed-charge memory devices. By enabling charge repositioning in response to externally applied voltages, the phototransistor achieves an unprecedented degree of control over memory retention timescales—a property rarely realized in optoelectronic components designed for AI applications.

From an energy perspective, co-localizing sensing and computation reduces data transfer bottlenecks traditionally plaguing AI processors. This attribute directly addresses pressing challenges in AI hardware, where escalating computational demands often collide with limitations in battery life and thermal management. As a result, the novel phototransistor aligns with broader efforts to develop sustainable, high-performance AI technologies.

Conceived through a multidisciplinary collaboration spanning electrical engineering and physical sciences at Oregon State University, the device’s development also underscores the growing convergence of materials science with computational intelligence. By synergizing metal oxide semiconductors with organic tetracene compounds, the team harnessed complementary material properties—robust electron transport with efficient light absorption—to realize the device’s multifunctional capabilities.

The National Science Foundation’s support, alongside the efforts of researchers including Larry Cheng, Ahasan Ullah, Tasnim Sarker, Xueqiao Zhang, Andrew Ensinger, Lizhong Chen, Roshell Lamug, and Oksana Ostroverkhova, culminated in the publication of their findings in the esteemed journal Advanced Functional Materials. The study not only charts new territory in optoelectronics but also sets the stage for transformative innovation in neuromorphic computing hardware.

As artificial intelligence increasingly demands systems capable of real-world adaptability and resource efficiency, innovations like this gate-tunable neuro-phototransistor epitomize the direction forward. By drawing directly from the brain’s principles of memory modulation and energy-efficient computation, the OSU team’s technology heralds a new era where AI devices can perceive, process, and remember in ways that approximate human cognition more closely than ever before.

Subject of Research: Not applicable

Article Title: Neuromodulator-Inspired Gate-Tunable Tetracene–Metal Oxide Phototransistor for Adaptive Optoelectronic Memory and Neuromorphic Computing

News Publication Date: 19-May-2026

Web References: http://dx.doi.org/10.1002/adfm.75942

References: Published in Advanced Functional Materials

Image Credits: Oregon State University

Keywords

Neuromorphic computing, phototransistor, adaptive memory, optoelectronics, brain-inspired AI, gate-tunable memory, oxide semiconductor, organic photosensitive material, energy-efficient AI, in-sensor computing, tetracene, neuromodulation