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BIOMIMETICS

Biomimetics is the application of biological methods and systems found in nature to the study and design of engineering systems and modern technology.
Also known as Bionics, biognosis, biomimicry, or bionical creativity engineering.
Biomimetics is being recognized as the technology of the future with increasing interest and funding in Europe, Japan, and the USA.
Global companies like Ford, General Electric, Herman Miller, HP, IBM, and Nike are collaborating with scientists to explore novel technologies.

BIOMIMETICS-Strategies and principles

Nature fits form to function, utilizes non-orthogonal forms and design methods to ensure maximization in structural efficiency.
It minimizes material input.
Nature recycles everything, using waste as a resource.
Nature uses an ordered hierarchy of structures.
Nature banks on diversity, constantly mutating and adapting.
Nature self assembles and generates structural organization on all scales.
Nature is resilient to changes and self-healing.
Nature optimizes rather than maximizes, using the least materials for optimal structure and function.

BIOMIMETICS- APPLICATION

CONSTRUCTION: Termite Den = Self-Cooling Office Building
ENERGY: Whale Edged Fins = Energy Efficient Turbine Blades
MEDICAL: Shark Skin Structure = Anti-bacterial Surface
PACKAGING: Burrs of Burdock = Velcro (hook and loop fastener)
MOBILITY: Kingfisher beak = Low resistance/noise Train Design
SELF-CLEANING: Lotus Leaves = Hydrophobic Paints/Surfaces

BIOMIMETICS- APPLICATION

Shark skin is constructed of overlapping scales.
Nature ensures water flows efficiently over the scales, helping sharks reach high speeds.

BIOMIMETICS- APPLICATION

Special alignment and grooved structure of denticles in shark skin decrease drag and increase swimming proficiency.
Airbus fuel consumption is down 1.5% when “shark skin” coating is applied to the aircraft.
It is possible to increase the efficiency of airplanes up to 4% by adjusting riblets.
Brings increase of speed up to 1.56%.
The results of the use of riblets are:
- Reduction of the total drag.
- A higher glide ratio.
- Better handling of the aircraft.

BIOMIMETICS- APPLICATION

Small hooks enable seed-bearing burrs to cling to tiny loops in the fabric.
Velcro fastening was invented in 1941 by Swiss engineer George de Mestral, inspired by burrs sticking to his dog's hair.
Under the microscope, he noted tiny hooks on the end of the burr's spines that caught anything with a loop.
The 2-part Velcro fastener system uses strips or patches of a hooked material opposite strips or patches of a loose-looped weave of nylon that holds the hooks.

BIOMIMETICS- APPLICATION

The kingfisher's beak became the model for the nose cone of Japan's 500 Series Shinkansen bullet train.

BIOMIMETICS- APPLICATION

Japan’s Shinkansen Bullet Train, the fastest train in the world at speeds of up to 200 miles per hour, was a marvel of modern technology.
However, there was noise issues. Each time the train emerged from the tunnel, it caused thunder-like sounds.
The train’s chief engineer, inspired by the shape of a bird’s beak, designed a more aerodynamic train.
The resulting design was based on the narrow profile of a kingfisher’s beak, resulting in a quieter train that also consumes 15% less electricity and goes 10% faster than before.

BIOMIMETICS- APPLICATION

The Gecko is a nocturnal lizard which has adhesive pads on its feet to assist in climbing on smooth surfaces.
Geckos hang single-toed from walls and walk along ceilings using fine hairs on feet.
Gecko’s feet are comprised of lamellae. Lamellae are equipped with setae; each seta ends in a spatula-like structure.
Nanoscale spatulae interact with wall atoms, generating Van der Waal’s forces. The adhesive system demonstrates high friction.

BIOMIMETICS- APPLICATION

Gecko Tape is a material covered with nanoscopic hairs that mimic those found on the feet of gecko lizards.
These millions of tiny, flexible hairs exert van der Waals forces that provide a powerful adhesive effect. One square centimeter of gecko tape could support a weight of one kilogram.
University of California - Berkeley created an array of synthetic micro-fibers using very high friction to support loads on smooth surfaces.
Gecko-footed robots could climb to the roof and emplace permanent anchors for suspension of utilities, transportation, or even entire lunar bases.

BIOMIMETICS- APPLICATION

A butterfly’s wings are one of nature’s most remarkable materials.
These tiny but complex structures reflect light in such a way that specific wavelengths interfere with each other to create intensely vivid colors.
By carefully studying this process, engineers at Qualcomm have been able to mimic this effect, allowing them to develop a system that produces colored electronic screens that are extremely efficient and can be viewed under any light conditions.

BIOMIMETICS- APPLICATION

The ability to squeeze through tight spaces and turn on a dime makes the spider an ideal model for lifesaving robots.
Researchers at Germany’s Frauenhofer Institute say this robot can be cheaply reproduced using 3D printers.
After natural catastrophes, industrial or reactor accidents, or in fire department sorties, it can help responders, for instance, by broadcasting live images or tracking down hazards or leaking gas.

BIOMIMETICS FOR SPACE APPLICATIONS

The Space environment presents a challenging setting due to existing conditions of low to zero gravity, high-temperature fluctuations, elevated levels of UV, electromagnetic and particulate radiation, reactive atomic oxygen, as well as natural micrometeoroids and space debris.
There are recent advances in biomimetic research and developments within the space industry.
Due to highly fluctuating temperatures within the space environment, enormous range of extremes, heat flow as well as temperature management and control are crucial steps to maintain the integrity of space systems.
Bio-inspired porous carbon showed promising results regarding their thermal protection of spacecrafts during re-entry processes into planetary atmospheres.
Lightweight and flexible materials have recently been developed for the protection of structures and equipment against electromagnetic radiation.
Experiments show that electromagnetic interference can be successfully shielded by substituting conventional metal shields with ones inspired by cellular architecture with tiny pores mimicking cell walls as aerogels.
Landing of unmanned spacecrafts on the surface of another planet is violent and associated with enormous impact forces.
Several actions and measures have been taken to protect sensitive equipment and payloads against those forces.
The peel of the pomelo fruit demonstrates a thick layer with open cell foam structure of varying pore size which protects the fruit inside from damage when falling from trees.
This impact damping and energy dissipating capabilities are implemented in artificial versions of the foam to apply in space systems.
As space debris has become a major topic of concern, recent efforts have concentrated on space debris removal and mitigation measures.
Robotic systems inspired by octopi arms have already been proposed for space debris removal.
Their great mobility, maneuverability, and adaptability make them very suitable to wrap around complex target shapes.
Seahorses use their tail for grasping activities involving different diameter objects.
Arrangement like continuously decreasing square cross-section in their tail made from four individual plates connected through special joints, provide great bending and torsion abilities for grasping, especially of a diverse range of shapes and sizes.
In addition, due to specialized construction, their tails show great fracture resistances under crushing and impact forces.

BIOMIMETICS OTHER APPLICATIONS

GreenPod Labs have created bio-inspired packaging sachets that mimic the built-in defense mechanisms within specific fruits or vegetables, in order to slow down the ripening rate and minimize microbial growth.
These are called plant-based volatiles, and the right formulation reduces the need for cold storage and cold supply chains.
Amphibio is using textiles made from one source material and create new recyclable and PFC-free alternatives for the outdoor and sportswear industry. Removing the need for any chemical treatments, Amphibio has mitigated two of the biggest barriers to sustainable textile production today - one material and no need for any chemical treatments.

BIOMIMETICS OTHER CURRENT APPLICATIONS

Intropic Materials is solving plastic waste from the inside out by embedding enzymes directly inside specific plastics to speed up natural degradation.
These plastics break down at the end of use into biodegradable or chemically recyclable small molecules without producing microplastics, in accessible life-friendly conditions like warm water baths or compost.

BIOINSPIRED ANN

A biological neuron has three types of main components: dendrites, soma (or cell body), and axon.
Dendrites receive signals from other neurons.
The soma sums the incoming signals. When sufficient input is received, the cell fires, transmitting a signal over its axon to other cells.
In the human brain, a typical neuron collects signals from others through a host of fine structures called dendrites.
The neuron sends out spikes of electrical activity through a long, thin strand known as an axon, which splits into thousands of branches.
At the end of each branch, a structure called a synapse converts the activity from the axon into electrical effects that inhibit or excite activity in the connected neurons.
Artificial Neural Network (ANN) is an information processing system that has certain performance characteristics in common with biological nets.
Several key features of the processing elements of ANN are suggested by the properties of biological neurons:
- The processing element receives many signals.
- Signals may be modified by a weight at the receiving synapse.
- The processing element sums the weighted inputs.
- Under appropriate circumstances (sufficient input), the neuron transmits a single output.
- The output from a particular neuron may go to many other neurons.
The strength of the connection between the neurons is stored as a weight-value for the specific connection.
Learning the solution to a problem = changing the connection weights.
ANNs have been developed as generalizations of mathematical models of neural biology, based on the assumptions that:
- Information processing occurs at many simple elements called neurons.
- Signals are passed between neurons over connection links.
- Each connection link has an associated weight, which, in typical neural net, multiplies the signal transmitted.
- Each neuron applies an activation function to its net input to determine its output signal.
A neuron receives input, determines the strength or the weight of the input, calculates the total weighted input, and compares the total weighted with a value (threshold).
The value is in the range of 0 and 1.
If the total weighted input is greater than or equal to the threshold value, the neuron will produce the output, and if the total weighted input is less than the threshold value, no output will be produced.
Architecture:
- A pattern of connections between neurons.
  - Single Layer Feedforward
  - Multilayer Feedforward
  - Recurrent
Strategy/Learning Algorithm:
- A method of determining the connection weights.
  - Supervised
  - Unsupervised
  - Reinforcement
Activation Function:
- Function to compute output signal from input signal
Some Properties of Artificial Neural Networks:
- Assembly of simple processors
- Information stored in connections
- Massively Parallel
- Massive connectivity
- Fault Tolerant
- Learning and Generalization Ability
- Robust
- Individual dynamics different from group dynamics. All these properties may not be present in a particular network.
Input Layer:
- The activity of the input units represents the raw information that is fed into the network.
Hidden Layer:
- The activity of each hidden unit is determined by the activities of the input units and the weights on the connections between the input and the hidden units.
Output Layer:
- The behavior of the output units depends on the activity of the hidden units and the weights between the hidden and output units.
The hidden units are free to construct their own representations of the input.
The weights between the input and hidden units determine when each hidden unit is active, and so by modifying these weights, a hidden unit can choose what it represents.
P-NET is a neural network architecture that encodes different biological entities into a neural network language with customized connections between consecutive layers (that is, features from patient profile, genes, pathways, biological processes, and outcome).
The trained P-NET provides a relative ranking of nodes in each layer to inform the generation of biological hypotheses.
Solid lines show the flow of information from the inputs to generate the outcome, and dashed lines show the direction of calculating the importance score of different nodes.
Candidate genes are validated to understand their function and mechanism of action.
Inspired by the five primary sensory systems (vision, touch, hearing, smell, taste) in the human MSeNN (bioinspired spiking multisensory neural network) and their interaction via neural networks, the artificial MSeNN consists of five artificial sensory systems and their integration via ANNs.
Sensors (photodetectors, pressure sensors, sound detectors, and simulated smell and taste receptors) convert external stimuli to potentials.
Spike encoders encode potentials into optical spikes for communication.
The transmitted information is decoded, filtered, and memorized by photomemristors, and the signals are crossmodally integrated and associated by ANNs for crossmodal recognition and imagination.
Spectrograms convert the audio inputs into 13 × 3-dimensional features feeding the ANN.
Visual data processed by 12 × 12 photodetectors and photomemristors, together with olfactory and gustatory vectors, are encoded into 12-dimensional features via an autoencoder to represent the image, smell, and taste information.
The ANN consists of 4 layers with 39 input, 12 hidden, 12 hidden, and 12 output neurons (image/smell/taste representation).
The memorized vision, smell, and taste vectors are encoded into the representations via the autoencoder to supervise the training of the ANN with audio inputs.
Recognized and reproduced image, smell, and taste of an apple, pear, blueberry, and the reproduced image of a heart and dog upon associated audio input.
Illustration of supervised training of the auditory-vision system using colors and apples.
Imagination of a blue apple by the trained system when audio input is given after training.

BIOSENSORS

Biosensors can be defined as analytical devices which include a combination of biological detecting elements like sensor systems and a transducer.
The sensitive biological element, e.g., tissue, microorganisms, organelles, cell receptors, enzymes, antibodies, nucleic acids, etc., is a biologically derived material that interacts with, binds with, or recognizes the analyte under study.
Biosensors are ubiquitous in biomedical diagnosis, point-of-care monitoring of treatment and disease progression, environmental monitoring, food control, drug discovery, forensics, and biomedical research.
A wide range of techniques can be used for the development of biosensors.
Their coupling with high-affinity biomolecules allows the sensitive and selective detection of a range of analytes.

APPLICATION AREAS

Soil Quality Monitoring
Environmental Monitoring
Disease Detection
Water Monitoring
Food Quality Monitoring
Pathogen Discovery
Toxin Detection
Drug Discovery
Analyte:
- A substance of interest that needs detection.
- For instance, glucose is an ‘analyte’ in a biosensor designed to detect glucose.
Bioreceptor:
- A molecule that specifically recognizes the analyte is known as a bioreceptor.
- Enzymes, cells, aptamers, deoxyribonucleic acid (DNA), and antibodies are some examples of bioreceptors.
- The process of signal generation (in the form of light, heat, pH, charge, or mass change, etc.) upon interaction of the bioreceptor with the analyte is termed bio-recognition.
Transducer:
- The transducer is an element that converts one form of energy into another.
- In a biosensor, the role of the transducer is to convert the bio-recognition event into a measurable signal.
- This process of energy conversion is known as signalization.
- Most transducers produce either optical or electrical signals that are usually proportional to the amount of analyte–bioreceptor interactions.
- The transducer works in a physicochemical way: optical, piezoelectric, electrochemical, electrochemiluminescence, etc., resulting from the interaction of the analyte with the biological element, to easily measure and quantify.
Electronics:
- This is the part of a biosensor that processes the transduced signal and prepares it for display.
- It consists of complex electronic circuitry that performs signal conditioning such as amplification and conversion of signals from analog into the digital form.
- The processed signals are then quantified by the display unit of the biosensor.
Display:
- The display consists of a user interpretation system such as the liquid crystal display of a computer or a direct printer that generates numbers or curves understandable by the user.
- This part often consists of a combination of hardware and software that generates results of the biosensor in a user-friendly manner.
- The output signal on the display can be numeric, graphic, tabular, or an image, depending on the requirements of the end user.
In summary, a biosensor typically consists of a bio-receptor (enzyme/antibody/cell/nucleic acid), transducer component (semi-conducting material/nanomaterial), and electronic system which includes a signal amplifier, processor & display.
In a biosensor, the bioreceptor is designed to interact with the specific analyte of interest to produce an effect measurable by the transducer.
High selectivity for the analyte among a matrix of other chemical or biological components is a key requirement of the bioreceptor.
Classification:
- Based on bioreceptors:
  - Enzyme-based biosensor
  - Antibody-based biosensors
  - Aptamer-based biosensors
  - Whole Cells Biosensors
- Based on transducers:
  - Electrochemical biosensors
  - Electronic biosensors
  - Gravimetric biosensors
  - Thermal biosensors
  - Acoustic biosensors
  - Magnetic biosensors
  - Electrometers
- Based on technology:
  - Optical biosensors
  - Electrical biosensors
  - SRP Biosensors
  - Nano biosensors
  - Biosensors-on-chip
  - Mechanical biosensors
- Based on detection system:
  - Amperometric biosensors
  - Potentiometric biosensors
  - Voltammetric biosensors
  - Conductometric biosensors
  - Impedometric biosensors
The first 'true' biosensor was developed by Leland C. Clark in 1956 for oxygen detection.
He is known as the ‘Father of Biosensors' and his invention of the oxygen electrode bears his name: 'Clark electrode'
Selectivity:
- It is the most important feature of a biosensor.
- Selectivity is the ability of a bioreceptor to detect a specific analyte in a sample containing other admixtures and contaminants.
Reproducibility:
- Reproducibility is the ability of the biosensor to generate identical responses for a duplicated experimental set-up.
- It is characterized by the precision and accuracy of the transducer and electronics in a biosensor.
- Precision is the ability of the sensor to provide alike results every time a sample is measured, and accuracy indicates the sensor's capacity to provide a mean value close to the true value when a sample is measured more than once.
Stability:
- It is the degree of susceptibility to ambient disturbances in and around the biosensing system.
- These disturbances can cause a drift in the output signals of a biosensor under measurement causing an error in the measured concentration and can affect the precision and accuracy of the biosensor.
Sensitivity:
- The minimum amount of analyte that can be detected by a biosensor defines its limit of detection (LOD) or sensitivity.
- In a number of medical and environmental monitoring applications, a biosensor is required to detect analyte concentration of as low as ng/ml or even fg/ml to confirm the presence of traces of analytes in a sample.
Linearity:
- Linearity shows the accuracy of the measured response (for a set of measurements with different concentrations of analyte) to a straight line, mathematically represented as $y=mc$ , where c is the concentration of the analyte, y is the output signal, and m is the sensitivity of the biosensor.
- Linearity of the biosensor is associated with the resolution of the biosensor and range of analyte concentrations under test.
- The resolution of the biosensor is the smallest change in the concentration of an analyte that is required to bring a change in the response of the biosensor.
Linearity:
- Linearity of the sensor should be high for the detection of high substrate concentration.
Sensitivity:
- Value of the electrode response per substrate concentration
Selectivity:
- Chemical interference must be minimized for obtaining a correct result
Response time:
- Time necessary for having 95% of the response

ELECTROCHEMICAL BIOSENSORS

Schematic representation of electrochemical biosensors based on different biochemical receptors and detection probes.
Amperometric biosensing of metabolite targets based on an enzyme electrode, including the current–time ( $i–t$ ) curve and the i signal for quantification.
Voltammetric biosensing of proteins or nucleic acids using an antibody-modified or nucleic acid-modified electrode through multistep sandwich sensing, one-step binding-induced folding sensing, or one-step proximity binding-based affinity sensing, including the current–potential ( $i–E$ ) curve and $i$ signal for target quantification.
Ion-selective electrodes with three different structures, including recording of the potential (E) for target quantification. d, Two types of organic electrochemical transistor sensors prepared by immobilizing the recognition element on the channel surface or on the gate electrode (G) for sandwich immunoassays of proteins, including recording of the channel current ( $i_{channel}$ ) for target quantification.
Photoelectrochemistry biosensing based on a three-electrode system and a light source, including recording of the photoelectrode photocurrent ( $i_{pc}$ ) upon target recognition for quantification.
Electrochemiluminescence biosensing of cells based on an aptamer-modified electrode through a sandwich-sensing format, including light intensity ( $I_{ECL}$ ) at excited potential by a photomultiplier tube (PMT) or imaging using a camera for target quantification.
Integration of electrochemical biosensors in portable, wearable, and implantable devices. CE, counter electrode; D, drain electrode; $Med<em>{ox}$ , oxidized form of mediator; $Med</em>{red}$ , reduced form of mediator; RE, reference electrode; S, source electrode; WE, working electrode.

OPTICAL BIOSENSORS

PIEZOELECTRIC BIOSENSORS

BIOSENSORS

Portable electrochemical biosensing devices.
Portable blood glucose meter consisting of a handheld electrochemical detector and disposable test strips.
The test strip contains a bottom electrode layer, an adhesive spacer layer, and a hydrophilic cover layer. The blood sample is introduced to the reaction chamber by capillary force.
Wearable sensors can be applied to monitor health-related or disease-related analytes in different body fluids, including tears, saliva, and sweat.
Health management can be based on continuous monitoring using wearable devices, including electrochemical biosensors, power supply, and wireless communication modules. BC, biocapacitor; BFC, biofuel cell; PENG, piezoelectric nanogenerator; TENG, triboelectric nanogenerator.
Google and Novartis's Alcon eye-care division are jointly developing a smart contact lens to help diabetics track their blood sugar levels by measuring glucose in tears and sending the data to a mobile device.
Global market statistics:
- Market value (2022): > $28.5 BN$
- Market value (2032): > $58 BN$
- CAGR (2023-32): >7%
Infectious disease testing segment:
- Market value (2032): > $2.5 BN$
- CAGR (2023-32): >7%
Non-wearable segment:
- Market value (2032): > $6 BN$
- CAGR (2023-32): >4.5%
Optical biosensors segment
Research laboratories segment
APAC market value (2032): > $16.3 BN$

3D Bioprinting

3D printing is driving major innovations in engineering, manufacturing, art, education, and medicine.
Recent advances have enabled 3D printing of biocompatible materials, cells, and supporting components into complex 3D functional living tissues.
3D bioprinting is being applied to regenerative medicine to address the need for tissues and organs suitable for transplantation.
3D bioprinting involves additional complexities, such as the choice of materials, cell types, growth and differentiation factors, and technical challenges related to the construction of tissues.
Addressing these complexities requires the integration of technologies from the fields of engineering, biomaterials science, cell biology, physics, and medicine.
3D bioprinting has already been used for the generation and transplantation of several tissues, including multilayered skin, bone, vascular grafts, tracheal splints, heart tissue, and cartilaginous structures.
Other applications include developing high-throughput 3D-bioprinted tissue models for research, drug discovery, and toxicology.
3D printing was first described in 1986 by Charles W. Hull. In his method, which he named ‘sterolithography’, thin layers of a material that can be cured with ultraviolet light were sequentially printed to form a solid 3D structure.
Development of solvent-free, aqueous based systems enabled the direct printing of biological materials into 3D scaffolds that could be used for transplantation.
A related development was the application of 3D printing to produce medical devices such as stents and splints for use in the clinic.
In a typical process for bioprinting 3D tissues, imaging of the damaged tissue and its environment can be used to guide the design of bioprinted tissues.
The choice of materials and cell source is essential and specific to the tissue form and function. These components have to integrate with bioprinting systems such as inkjet, microextrusion, or laser-assisted printers.
Bioprinting Scaffolds for clinical use:
- Digital 3D images obtained from CT, MRI, or ultrasound are used to design a suitable scaffold with 3D slicing and CAD software.
- Materials from printing are chosen depending on the application and can consist of polymers, ceramics, and bioactive components.
- Cells are selected depending on the application; a bioink can consist of singular or multiple cell types.
Steps in 3D bioprinting: different steps and stages that lead to the production of bioprinted constructs for implantation or in vitro testing.
Schematic of Inkjet-based Bioprinting. Thermal inkjet uses heat-induced bubble nucleation that propels the bioink through the micro-nozzle. Piezoelectric actuator produces acoustic waves that propel the bioink through the micro-nozzle.
Schematic of Stereolithography Bioprinting. Photopolymerization occurs on the surface of the vat where the light-sensitive bioink is exposed to light energy. Axial platform moves downward the Z-axis during fabrication. This layer-by-layer technique does not depend on the complexity of the design, rather on its height.
Schematic of Extrusion-based Bioprinting; from left, pneumatic-based and right, mechanical-based. Struts are extruded via pneumatic or mechanical pressure through micro-nozzles. Extrusion-based techniques can produce structures with great mechanical properties and print fidelity.
Two-dimensional tissue: skin
Hollow tubes: Trachea, Vasculature, Aortic valve, Tracheal splint
Cartilage
Solid organs : Kidney
The 3D bioprinting process.
- Medical imaging (CT, MRI)
- 3D CAD model
- Visualized motion program
- 3D printing process
A representative 3D reconstructed model of the osteoarthritic knee joint from the RESTORE Project's online database, together with STL models of the femoral cartilage with a horse-shoe-shaped cartilage patch designed to fit the lesion.
(Left): The horse-shoe-shaped cartilage patch model was created using various imaging techniques to unravel the 3D architecture of the tissues and control the positioning of print heads to place bioink in a 3D shape for a patient-specific match.
(Right): 3D bioprinting process of the patch using Brinter® Rotary Tool print head and bioink material mixed with cells, spheroids or organoids.
Organoids, Micro-organs
- Engineering living systems, micro-physiological systems, cellular machining, cell robots
Cell as building blocks.
In vitro biological models
- Tissue/disease/drug models, cell/organ-on-a-chip
Biocompatible, degradable, and absorbable
- Tissue scaffolds
  - Examples: Bone scaffolds, skin scaffolds
Biocompatible, but may not be degradable
- Permanent implants
  - Examples: hip replacements, artificial knees
No requirement for biocompatibility
- Bio-medical modeling, in vitro medical devices
  - Examples: RP models for surgical modeling, surgical planning
Modeling incorporates imaging data into the final 3D printed object.
- 3D scan
- Refine
- Print Path
- FRESH Bioprint
Proposed process for the generation of 3D heart valves through bioprinting to arrive at functional tissue-engineered heart valves

Image showing diabetic foot ulcer caused by delayed wound healing in diabetic patients.

3D cell printing system showing the materials and printing methods required for this study.
Modeling diabetic epidermis through intercellular interaction between diabetic human dermal fibroblasts and normal human epidermal keratinocytes, and a wounded skin model with delayed reepithelization.
Adding subcutaneous layer (blood vessel + fat) to better recapitulate pathophysiological functions of the diabetes and to test applicability to drug screening platform.
Personalized Medicine
- Develop patient-specific models
Cell Cultured Food
- Develop the next era of nutritional sources.
Drug Discovery
- Accelerate drug candidate discovery
Regenerative Medicine
- Recreate in vivo-like conditions
Market is expected to REGISTER a CAGR of 20.9%.
Market was valued at USD 1.3 Billion in 2022
Market value in 2032 will reach >USD 7.2 Billion
Approx. 21.3% of global market revenue was accounted for by North America in 2022
Based on End-use, the Research Organizations and Academic Institutes segment is expected to register a CAGR of 20.7%
The market is FRAGMENTED with key players accounting for the majority of market revenue.
One of the KEY drivers for market revenue growth is rising use in cosmetology and pharmaceutical industries.

BIOENTREPRENEURSHIP

An entrepreneur is a person who starts an enterprise.
An entrepreneur is someone who perceives opportunity, organizes resources needed for exploiting that opportunity, and exploits it.
Characteristics of an entrepreneur include spontaneous creativity, the ability and willingness to make decisions in the absence of solid data, and a generally risk-taking personality.
An entrepreneur may be driven by a need to create something new or build something tangible.
Entrepreneurship can be described as a process of action an entrepreneur undertakes to establish his enterprise.
Entrepreneurship is a creative activity.
It is the ability to create and build something from practically nothing.
It is a knack of sensing opportunity where others see chaos, contradiction, and confusion.
Entrepreneurship is the attitude of mind to seek opportunities, take calculated risks, and derive benefits by setting up a venture.
It comprises numerous activities involved in conception, creation, and running an enterprise.
According to Peter Drucker, Entrepreneurship is defined as ‘a systematic innovation, which consists in the purposeful and organized search for changes, and it is the systematic analysis of the opportunities such changes might offer for economic and social innovation.’
The entrepreneurial role encompasses the following responsibilities:
- Perception of market opportunities
- Gaining command over scarce resources
- Purchasing inputs
- Marketing the products
- Dealing with bureaucrats
- Managing human relations within the firm
- Managing customer and supplier relations
- Managing finance
- Managing production
- Acquiring and overseeing assembly of the factory
- Industrial engineering
- Upgrading process and product
- Introducing new production techniques and products
Competencies of an Entrepreneur:
- Initiative
- Creativity and Innovation
- Risk Taking and Risk Management
- Problem Solving:
- Leadership
- Persistence
- Quality Performance
- Information Seeking
- Systematic Planning
- Persuasion and Influencing Others
- Enterprise Launching Competencies
- Enterprise Management Competencies
Bioentrepreneurship can be described as the creation of wealth derived from the application of the biosciences to the business context.
Bioentrepreneurs look for commercial value in every aspect of the technology that they utilize.
Innovativeness is vital to the creation of a biotechnology venture, while credibility remains the backbone of the bioentrepreneur’s character.
The challenges of financing from venture capitalists prove to be a constant struggle for the bioentrepreneur.
Similarly, risk-taking comes from dealing with the uncertainties of R&D, a rapidly evolving marketplace, and the nebulous field of intellectual property.
Opportunity spotting by analyzing the needs and problems that exist in the environment
Evaluating the ideas received from different sources to find a creative solution
Identifying a product or service through innovation
Setting up a project and nurturing it to success.
Bioentrepreneurship is a dynamic and multifaceted field that sits at the intersection of biotechnology, business, and innovation.
It encompasses the art and science of transforming scientific discoveries into viable commercial ventures.
Bioentrepreneurship bridges the gap between laboratory research and market-ready products. It thrives on the synergy between scientific breakthroughs and entrepreneurial vision.
Bioentrepreneurs must navigate complex