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Design Synthesis: What it is and How to Apply it to a Design Process

By robert jarrell.

The Magical Part of Design

There has been a few occasions over the course of my design career when I’m asked how I come up with a design solution and know it’s the right one. I’ve never had a satisfactory answer for this because it’s too complex to explain succinctly, so I usually answer with something as simple as “creativity and validation,” which is not far off the mark, but rather vague. A better answer, at least in the context of product, software, and website design is synthesis , which is the magical part of design that uses insights from data to generate epiphanies and inform design decisions.

Stepping Away From the Problem

As magical as synthesis can be it is often the invisible part of the design process that team members and clients don’t see because it occurs in isolation: in the designer’s mind, on our computers, or by incubation. The latter especially make synthesis unpredictable, difficult to anticipate, and challenging to time-box. But incubation is also intuitive and natural—we all use it to solve all types of problems from relationships with people to important life-changing decisions. Incubation is the act of stepping away from the problem to allow time to pass so ideas can percolate. This is one reason why time-boxing design doesn’t always yield the right solution; it produces the best solution that can be accomplished within a confined period of time, but limits the amount of synthesis that can occur. However, even though synthesis does rely on incubation, it can be structured by using inference-based sense-making methods to help generate ideas more quickly and enable team members and clients to participate in the process as well.

Synthesis & Ethnography

The essence of design is to solve problems and the best way to achieve that is by having a process that includes synthesis because it bridges gaps between different phases of learning and problem solving. Another word for design research is ethnography, which enables a designer to immerse themselves in a problem, culture, or domain in order to understand it. This is often observational and achieved by studying people’s behavior, but can also be done by conducting interviews and asking people questions or by contextual inquiry in order to understand how people solve problems in their own setting or workplace. Ethnography gathers real data on real people and attempts to understand their relationship to a problem instead of a designer or stakeholder making assumptions based on personal points of view and mental models.

Inference-based Sense Making & Logic

Once we have gathered data we can flag the most profound insights and use inference-based sense-making to formulate logical ways of thinking about the problem. There are three forms of logic (or reasoning) that are true for us as human beings that we can use as designers. These are:

Deductive logic is a self contained logic. Its reasoning is if the premise is true then the conclusion is true. A simple example of deductive reasoning is:

Since all humans are mortal and I am a human then I am mortal

As humans we are really good at using deductive logic. For example, we use it all the time to pick apart a movie or a book that sometimes breaks the premises it has set forth.

Inductive Logic is evidence based reasoning that uses the formula that it is true until it’s not. It is logic that recognizes there is a probable chance of inaccuracy. Here is a simple example of inductive reasoning:

All basketball players I saw at the game were tall therefore all basketball players must be tall

We know this logic breaks down when we go to a high school basketball game or pickup game at the local court, but we hold onto this logic until a situation presents itself to prove our reasoning is wrong. Both deductive and inductive reasoning form the backdrop for how most scientific studies are done.

Abductive logic is inference based and relies on circumstance, experience, and observation. Unlike deductive reasoning the premises do not guarantee the conclusion. However, its power comes from using its own reasoning combined with deductive and inductive logic to form a series of statements. Here’s an example:

I’m driving on the freeway and suddenly traffic comes to a halt. A mile away I see police lights. There must have been an accident. (Inductive reasoning)

Traffic continues to be at a crawl for 15 minutes. It must have been an accident. (Inductive reasoning)

I hear more sirens and see an ambulance head towards the scene. The accident must be serious and just recently occurred, which means I may have to wait awhile. (Inductive and abductive reasoning)

Throughout my whole life experience whenever something similar happened it was an accident. (Abductive reasoning)

I know from experience that if I’m patient and wait this through I will eventually drive pass the accident and traffic will become normal again. (Abductive reasoning)

Another option is that if there is an exit coming up I can get off the freeway and it will save me time. (Abductive reasoning)

I see an exit and decide that since this is a serious accident that has just occurred it may take awhile to clear up so I’ll get off at the next exit and take a local route to my destination. (One possible solution to the problem)

We use abductive logic all the time throughout out our daily lives and are mostly unconscious of it, but synthesis relies heavily on this form of logic to draw conclusions in order to make a logical progression and generate solutions to a problem.

Synthesis As Learning Process

As designers we use sense-making in our process to make connections between insights (data) that may not have been connected before to forge new relationships, meaning, and concepts. Another way of saying the same thing is that synthesis provides us with a learning process—we learn about a problem so we can adequately solve it.

The process of learning has four phases:

• Information

These phases of learning do not always occur as a linear progression. Any of these four can occur during different phases of design because often the process is like a spiral as we iterate and loop through phases. However, we do aim to cross one phase of learning to the next by bridging gaps between them. This benefits the designer because by the very act of bridging we connect various insights (obtained by research and ethnography) that have the potential to be epiphanies, those “aha moments” when something profound is discovered.

Working With Data

As a designer I will often begin a project by doing research and looking at data. Some of this data might come from the client during the discovery phase. They might provide wireframes, powerpoint decks, marketing segmentation reports, strategic positioning statements, product specifications, interaction models, and analytics—all this data they’ve gathered is mostly quantitative and can be used to create hypotheses.

Sometimes client data is based on a handful of stakeholder’s personal views and assumptions about how things should work and how they will solve problems for people they’ve most likely never talked to or don’t clearly understand. The problem with relying on stakeholder data is it can end up solving the wrong problem or it can solve the correct problem the wrong way.

On the other hand there is human data that can be obtained by research and ethnography, which allows designers to understand real people’s needs, pain points, and mental models so we can solve the right problem successfully.

One of my main objectives as a designer is to understand stakeholders’ points of view and their business goals and also get data from people who use the product or have a need for it. The challenging part of this process is bridging gaps of information. This is mostly a problem because of either a lack of data, a lack of time, or both. But it’s also a problem because it can be overwhelming trying to work with the data you do get. Synthesis provides a way to work with data so we can move forward to other learning phases. One point I want to stress is that it’s more important to get user data because it gives you the best chances for success—meaning you will actually solve the problem the right way for the right people.

Different Synthesis Techniques

When designers bridge the gap between the four phases of learning we are actually synthesizing data so we can connect insights, generate epiphanies, and develop hypotheses. There are different techniques to do this, which I’ll describe below for each learning phase.

Synthesizing Data to Information

The best thing to do in this phase is get data out of our computers and onto a wall so we can move it around and start to form relationships. By doing this we start to see data from a bird’s eye view rather than having tunnel vision that computer screens limit us to. It allows us to see patterns and themes and helps us understand problems more clearly.

We can also turn data into information by making diagrams which force us to look at relationships and make design decisions. These can be thinking sketches, flow charts, or mind maps. We can interpret the data and therefore assign it meaning. We can use the technique of asking “why?” over and over again. This is abductive reasoning, the seed of a new idea that may or may not be correct, but still allows us to consider the problem in a way that may not have been apparent before.

Some methods that can be used to move data to information are:

• Affinity diagramming
• Experience mapping
• Flow diagramming

Synthesizing Information to Knowledge

This is mainly concerned with conveying the user experience, which is achieved by understanding people’s behaviors, attitudes, and pain points when they try to complete their goals using a product. The best way to have this understaning is to first make sure we have data based on user interviews, contextual inquiries, or using various techniques of testing such as exploratory or usability testing.

Another synthesis method that can be used is to change perspective by zooming in and out of the problem. In software design we do this by shifting the problem from high to granular levels. We might make a product flow chart to capture how the overall software works and a wireframe to understand a specific area’s information architecture, UI, and content. We can also shift context to consider the different states the product can be experienced such as a first time user versus power user. Furthermore, we can consider error states and anticipate how a product might scale over time.

Some other methods that can be used to move data from information to knowledge are:

• Concept Mapping
• Persona Development
• Storyboarding
• Scenario Development

Synthesizing Knowledge to Wisdom

This phase of learning utilizes what if? thinking and is generative. This is when blue sky ideas can emerge because we’ve studied the data and now we can use all forms of logic, especially abductive reasoning. We can also use lateral thinking as a method of considering the problem in a different light. The more diverse and cross functional a team is on a project the better chances lateral thinking will be effective because people’s various cultural backgrounds and interest can bridge relationships between meaning. We also cross that bridge by having empathy, which is provided by the data we’ve gathered on people to understand their desires, needs, pain points, and mental models.

Some other methods that can be used are:

• Insight combination
• Participatory design

Understanding Data to Solve Problems

Synthesis is the magical part of the design process that allows us to understand data so we can solve problems for businesses and people. It relies heavily on abductive reasoning, which is based on sense-making and can draw on deductive and inductive logic. Synthesis is the process of learning, which provides us with insights that bridge relationships between data, information, knowledge and wisdom. It ultimately allows us to understand the problem, domain, or culture we are concerned with and create a point of reference and rational whole so we can develop new concepts.

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February 21, 2017

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Analysis vs. Synthesis

What's the difference.

Analysis and synthesis are two fundamental processes in problem-solving and decision-making. Analysis involves breaking down a complex problem or situation into its constituent parts, examining each part individually, and understanding their relationships and interactions. It focuses on understanding the components and their characteristics, identifying patterns and trends, and drawing conclusions based on evidence and data. On the other hand, synthesis involves combining different elements or ideas to create a new whole or solution. It involves integrating information from various sources, identifying commonalities and differences, and generating new insights or solutions. While analysis is more focused on understanding and deconstructing a problem, synthesis is about creating something new by combining different elements. Both processes are essential for effective problem-solving and decision-making, as they complement each other and provide a holistic approach to understanding and solving complex problems.

Further Detail

Introduction.

Analysis and synthesis are two fundamental processes in various fields of study, including science, philosophy, and problem-solving. While they are distinct approaches, they are often interconnected and complementary. Analysis involves breaking down complex ideas or systems into smaller components to understand their individual parts and relationships. On the other hand, synthesis involves combining separate elements or ideas to create a new whole or understanding. In this article, we will explore the attributes of analysis and synthesis, highlighting their differences and similarities.

Attributes of Analysis

1. Focus on details: Analysis involves a meticulous examination of individual components, details, or aspects of a subject. It aims to understand the specific characteristics, functions, and relationships of these elements. By breaking down complex ideas into smaller parts, analysis provides a deeper understanding of the subject matter.

2. Objective approach: Analysis is often driven by objectivity and relies on empirical evidence, data, or logical reasoning. It aims to uncover patterns, trends, or underlying principles through systematic observation and investigation. By employing a structured and logical approach, analysis helps in drawing accurate conclusions and making informed decisions.

3. Critical thinking: Analysis requires critical thinking skills to evaluate and interpret information. It involves questioning assumptions, identifying biases, and considering multiple perspectives. Through critical thinking, analysis helps in identifying strengths, weaknesses, opportunities, and threats, enabling a comprehensive understanding of the subject matter.

4. Reductionist approach: Analysis often adopts a reductionist approach, breaking down complex systems into simpler components. This reductionist perspective allows for a detailed examination of each part, facilitating a more in-depth understanding of the subject matter. However, it may sometimes overlook the holistic view or emergent properties of the system.

5. Diagnostic tool: Analysis is commonly used as a diagnostic tool to identify problems, errors, or inefficiencies within a system. By examining individual components and their interactions, analysis helps in pinpointing the root causes of issues, enabling effective problem-solving and optimization.

Attributes of Synthesis

1. Integration of ideas: Synthesis involves combining separate ideas, concepts, or elements to create a new whole or understanding. It aims to generate novel insights, solutions, or perspectives by integrating diverse information or viewpoints. Through synthesis, complex systems or ideas can be approached holistically, considering the interconnections and interdependencies between various components.

2. Creative thinking: Synthesis requires creative thinking skills to generate new ideas, concepts, or solutions. It involves making connections, recognizing patterns, and thinking beyond traditional boundaries. By embracing divergent thinking, synthesis enables innovation and the development of unique perspectives.

3. Systems thinking: Synthesis often adopts a systems thinking approach, considering the interactions and interdependencies between various components. It recognizes that the whole is more than the sum of its parts and aims to understand emergent properties or behaviors that arise from the integration of these parts. Systems thinking allows for a comprehensive understanding of complex phenomena.

4. Constructive approach: Synthesis is a constructive process that builds upon existing knowledge or ideas. It involves organizing, reorganizing, or restructuring information to create a new framework or understanding. By integrating diverse perspectives or concepts, synthesis helps in generating comprehensive and innovative solutions.

5. Design tool: Synthesis is often used as a design tool to create new products, systems, or theories. By combining different elements or ideas, synthesis enables the development of innovative and functional solutions. It allows for the exploration of multiple possibilities and the creation of something new and valuable.

Interplay between Analysis and Synthesis

While analysis and synthesis are distinct processes, they are not mutually exclusive. In fact, they often complement each other and are interconnected in various ways. Analysis provides the foundation for synthesis by breaking down complex ideas or systems into manageable components. It helps in understanding the individual parts and their relationships, which is essential for effective synthesis.

On the other hand, synthesis builds upon the insights gained from analysis by integrating separate elements or ideas to create a new whole. It allows for a holistic understanding of complex phenomena, considering the interconnections and emergent properties that analysis alone may overlook. Synthesis also helps in identifying gaps or limitations in existing knowledge, which can then be further analyzed to gain a deeper understanding.

Furthermore, analysis and synthesis often involve an iterative process. Initial analysis may lead to the identification of patterns or relationships that can inform the synthesis process. Synthesis, in turn, may generate new insights or questions that require further analysis. This iterative cycle allows for continuous refinement and improvement of understanding.

Analysis and synthesis are two essential processes that play a crucial role in various fields of study. While analysis focuses on breaking down complex ideas into smaller components to understand their individual parts and relationships, synthesis involves integrating separate elements or ideas to create a new whole or understanding. Both approaches have their unique attributes and strengths, and they often complement each other in a cyclical and iterative process. By employing analysis and synthesis effectively, we can gain a comprehensive understanding of complex phenomena, generate innovative solutions, and make informed decisions.

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Stage 2 in the Design Thinking Process: Define the Problem and Interpret the Results

An integral part of the Design Thinking process is the definition of a meaningful and actionable problem statement, which the design thinker will focus on solving. This is perhaps the most challenging part of the Design Thinking process, as the definition of a problem (also called a design challenge) will require you to synthesise your observations about your users from the first stage in the Design Thinking process, which is called the Empathise stage.

When you learn how to master the definition of your problem, problem statement, or design challenge, it will greatly improve your Design Thinking process and result. Why? A great definition of your problem statement will guide you and your team’s work and kick start the ideation process in the right direction. It will bring about clarity and focus to the design space. On the contrary, if you don’t pay enough attention to defining your problem, you will work like a person stumbling in the dark.

In the Define stage you synthesise your observations about your users from the first stage, the Empathise stage. A great definition of your problem statement will guide you and your team’s work and kick start the ideation process (third stage) in the right direction. The five stages are not always sequential — they do not have to follow any specific order and they can often occur in parallel and be repeated iteratively. As such, the stages should be understood as different modes that contribute to a project, rather than sequential steps.

Analysis and Synthesis

Before we go into what makes a great problem statement, it’s useful to first gain an understanding of the relationship between analysis and synthesis that many design thinkers will go through in their projects. Tim Brown, CEO of the international design consultancy firm IDEO, wrote in his book Change by Design: How Design Thinking Transforms Organizations and Inspires Innovation , that analysis and synthesis are “equally important, and each plays an essential role in the process of creating options and making choices.”

Analysis is about breaking down complex concepts and problems into smaller, easier-to-understand constituents. We do that, for instance, during the first stage of the Design Thinking process, the Empathise stage, when we observe and document details that relate to our users. Synthesis , on the other hand, involves creatively piecing the puzzle together to form whole ideas. This happens during the Define stage when we organise, interpret, and make sense of the data we have gathered to create a problem statement.

Although analysis takes place during the Empathise stage and synthesis takes place during the Define stage, they do not only happen in the distinct stages of Design Thinking. In fact, analysis and synthesis often happen consecutively throughout all stages of the Design Thinking process. Design thinkers often analyse a situation before synthesising new insights, and then analyse their synthesised findings once more to create more detailed syntheses.

What Makes a Good Problem Statement?

A problem statement is important to a Design Thinking project, because it will guide you and your team and provides a focus on the specific needs that you have uncovered. It also creates a sense of possibility and optimism that allows team members to spark off ideas in the Ideation stage, which is the third and following stage in the Design Thinking process. A good problem statement should thus have the following traits. It should be:

Human-centered. This requires you to frame your problem statement according to specific users, their needs and the insights that your team has gained in the Empathise phase. The problem statement should be about the people the team is trying to help, rather than focusing on technology, monetary returns or product specifications.

Broad enough for creative freedom. This means that the problem statement should not focus too narrowly on a specific method regarding the implementation of the solution. The problem statement should also not list technical requirements, as this would unnecessarily restrict the team and prevent them from exploring areas that might bring unexpected value and insight to the project.

Narrow enough to make it manageable. On the other hand, a problem statement such as , “Improve the human condition,” is too broad and will likely cause team members to easily feel daunted. Problem statements should have sufficient constraints to make the project manageable.

As well as the three traits mentioned above, it also helps to begin the problem statement with a verb, such as “Create”, “Define”, and “Adapt”, to make the problem become more action-oriented.

How to Define a Problem Statement

Methods of interpreting results and findings from the observation oriented Empathise phase include:

Space Saturate and Group and Affinity Diagrams – Clustering and Bundling Ideas and Facts

In space saturate and group, designers collate their observations and findings into one place, to create a collage of experiences, thoughts, insights, and stories. The term 'saturate' describes the way in which the entire team covers or saturates the display with their collective images, notes, observations, data, experiences, interviews, thoughts, insights, and stories in order to create a wall of information to inform the problem-defining process. It will then be possible to draw connections between these individual elements, or nodes, to connect the dots, and to develop new and deeper insights, which help define the problem(s) and develop potential solutions. In other words: go from analysis to synthesis.

Empathy Mapping

An empathy map consists of four quadrants laid out on a board, paper or table, which reflect the four key traits that the users demonstrated/possessed during the observation stage. The four quadrants refer to what the users: Said , Did , Thought , and Felt . Determining what the users said and did are relatively easy; however, determining what they thought and felt is based on careful observation of how they behaved and responded to certain activities, suggestions, conversations etc. (including subtle cues such as body language displayed and the tone of voice used).

Point Of View – Problem Statement

A Point Of view (POV) is a meaningful and actionable problem statement, which will allow you to ideate in a goal-oriented manner. Your POV captures your design vision by defining the RIGHT challenge to address in the ideation sessions. A POV involves reframing a design challenge into an actionable problem statement. You articulate a POV by combining your knowledge about the user you are designing for, his or her needs and the insights which you’ve come to know in your research or Empathise mode. Your POV should be an actionable problem statement that will drive the rest of your design work.

You articulate a POV by combining these three elements – user, need, and insight. You can articulate your POV by inserting your information about your user, the needs and your insights in the following sentence:

[ User . . . (descriptive)] needs [ need . . . (verb)] because [ insight. . . (compelling)]

“How Might We” Questions

When you’ve defined your design challenge in a POV, you can start to generate ideas to solve your design challenge. You can start using your POV by asking a specific question starting with: “ How Might We ” or “in what ways might we”. How Might We ( HMW ) questions are questions that have the potential to spark ideation sessions such as brainstorms. They should be broad enough for a wide range of solutions, but narrow enough that specific solutions can be created for them. “How Might We” questions should be based on the observations you’ve gathered in the Empathise stage of the Design Thinking process.

For example, you have observed that youths tend not to watch TV programs on the TV at home, some questions which can guide and spark your ideation session could be:

How might we make TV more social, so youths feel more engaged?

How might we enable TV programs to be watched anywhere, at anytime?

How might we make watching TV at home more exciting?

The HMW questions open up to Ideation sessions where you explore ideas, which can help you solve your design challenge in an innovative way.

Why-How Laddering

"As a general rule, asking 'why’ yields more abstract statements and asking 'how’yields specific statements. Often times abstract statements are more meaningful but not as directly actionable, and the opposite is true of more specific statements." – d.school, Method Card, Why-How Laddering

For this reason, during the Define stage designers seek to define the problem, and will generally ask why . Designers will use why to progress to the top of the so-called Why-How Ladder where the ultimate aim is to find out how you can solve one or more problems. Your How Might We questions will help you move from the Define stage and into the next stage in Design Thinking, the Ideation stage, where you start looking for specific innovative solutions . In other words you could say that the Why-How Laddering starts with asking Why to work out How they can solve the specific problem or design challenge.

The Take Away

The second stage in a typical Design Thinking process is called the Define phase. It involves collating data from the observation stage (first stage called Empathise ) to define the design problems and challenges. By using methods for synthesising raw data into a meaningful and usable body of knowledge — such as empathy mapping and space saturate and group — we will be able to create an actionable design problem statement or Point of View that inspire the generation of ideas to solve it. The How Might We questions open up to Ideation sessions where you explore ideas, which can help you solve your design challenge in an innovative way.

References & Where to Learn More

Course: “Design Thinking - The Ultimate Guide” .

d.school: Space Saturation and Group .

d.school: Empathy Map .

d.school: “How might we” questions .

Hero Image: Author/Copyright holder: gdsteam. Copyright terms and licence: CC BY 2.0

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Analysis + Synthesis

The core design process class at the Institute of Design; students will learn a flexible and systematic approach for problem solving.

Objective & Outcomes

The focus on methods has created the erroneous belief that in order to think like a designer you simply need to fill out a set of methods templates. The comprehensive problem-solving process (AKA design) is far more complex. This course will teach a three-phase process: 

• Start the process by thinking deeply about the issues involved (not the problem).
• Establish a point of view and a clear direction for moving forward. 
• Produce a detailed plan for the implementation of your intervention.

The process will act as a scaffolding for three levels of learning: design outcomes, ways of thinking, and specific practices.

• Design outcomes: what we build, the consequences, and the results of the process. 
• Ways of thinking: how we think, the modes and approaches we use to achieve the design outcomes. 
• Specific practices: what we do, the techniques and tools used during the process.

After completing this course, students will demonstrate:

• Command of a rigorous but flexible process that can be applied to most design problems 
• Demonstrated proficiency in producing intended design outcomes 
• Dexterity of thinking: from strategy to implementation details 
• Ability to select and apply appropriate practices during a design project

Typical Schedule

• Session 1 : Conduct Research
• Session 2 : Clustering & Building Frameworks
• Session 3 : Coding Data
• Session 4 : User Needs to Specs
• Session 5 : Agile / User Stories
• Session 6 : Systems
• Session 7 : Quantitative Reasoning
• Session 8 : How Might We & Design Principles
• Session 9 : Brainstorming Methods
• Session 10 : Systemic Exploration
• Session 11 : Concept Systems
• Session 12 : Decision Making
• Session 13 : Experiments & Prototyping

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• Design Thinking Tutorial
• Design Thinking - Home
• Design Thinking - Introduction
• Design Thinking - Definition
• Design Thinking - Applications
• Design Thinking - Solution-Based
• Analysis Vs Synthesis
• Design Thinking - Divergent
• Design Thinking - Convergent
• Design Thinking - Attributes
• Design Thinking - Empathize Stage
• Design Thinking - Define Stage
• Design Thinking - Ideate Stage
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Design Thinking - Analysis Vs Synthesis

In this chapter, we will see the difference between two ways of solution-based thinking, i.e. Analysis and Synthesis, and also get to know how it helps in design thinking.

Analysis is derived from the Greek word ‘analusis’, which translates into ‘breaking up’ in English. Analysis is older than the times of great philosophers like Aristotle and Plato. As discussed in the previous section, analysis is the process of breaking down a big single entity into multiple fragments. It is a deduction where a bigger concept is broken down to smaller ones . This breaking down into smaller fragments is necessary for improved understanding.

So, how does analysis help in design thinking? During analysis, design thinkers are required to break down the problem statement into smaller parts and study each one of them separately. The different smaller components of the problem statement are to be solved one-by-one, if possible. Then, solutions are thought for each of the small problems. Brainstorming is done over each of the solutions.

Later, a feasibility check is done to include the feasible and viable solutions. The solutions that don’t stand firm on the grounds of feasibility and viability are excluded from the set of solutions to be considered.

Design thinkers are, then, encouraged to connect with the diverse ideas and examine the way each idea was composed. This process of breaking down the bigger problem statement at hand into multiple smaller problem statements and examining each as a separate entity is called analysis.

Reductionism

The underlying assumption in analysis is reductionism . Reductionism states that the reality around us can be reduced down to invisible parts. The embodiment of this principle is found in basic axioms of analytic geometry, which says “the whole is equal to the sum of its parts”. However, understanding of a system cannot be developed by analysis alone. Hence, synthesis is required following analysis.

Synthesis refers to the process of combining the fragmented parts into an aggregated whole. It is an activity that is done at the end of the scientific or creative inquiry. This process leads to creation of a coherent bigger entity, which is something new and fresh. How does synthesis come into picture in design thinking?

Once the design thinkers have excluded the non-feasible and non-viable solutions and have zeroed-in on the set of feasible and viable solutions, it is time for the thinkers to put together their solutions.

Out of 10 available solutions, around 2-3 solutions may need to be excluded since they may not fit into the larger picture, i.e. the actual solution. This is where synthesis helps.

The design thinkers start from a big entity called the problem statement and then end up with another bigger entity, i.e. the solution. The solution is completely different from the problem statement. During synthesis, it is ensured that the different ideas are in sync with each other and do not lead to conflicts.

Analysis + Synthesis = Design Thinking

Analysis and synthesis, thus, form the two fundamental tasks to be done in design thinking. Design thinking process starts with reductionism, where the problem statement is broken down into smaller fragments. Each fragment is brainstormed over by the team of thinkers, and the different smaller solutions are then put together to form a coherent final solution. Let us take a look at an example.

Problem Statement − Suppose the problem statement at hand is to contain the attrition that happens in companies worldwide. High quality employees leave the organization, mainly after the appraisal cycle. As a result, an average company loses its valuable human resources and suffers from an overhead of transferring the knowledge to a new employee. This takes time and additional human resource in the form of a trainer, which adds to the company’s costs. Devise a plan to contain attrition in the company.

Analysis − Now, let’s break down the problem statement into various constituent parts. Following are the subparts of the same problem statement, broken down to elementary levels.

• The employees are not motivated anymore to work in the company.
• Appraisal cycle has something to do with attrition.
• Knowledge transfer is necessary for new employees.
• Knowledge transfer adds to the cost of the company.

Synthesis − Now, let's start solving each problem individually. In this step, we will do synthesis. Let's look at one problem at a time and try to find a solution only for that problem statement, without thinking of other problem statements.

To solve the problem of lack of motivation, the management can plan some sort of incentives that can be given on a regular basis. The efforts put in by the employees must be rewarded well. This will keep the employees motivated.

To solve the issue of occurrence of attrition during appraisal cycle, the management can conduct a meeting with the employees leaving the organization, and take their insight as to what led them to leave the company.

For knowledge transfer, the management can hire only those people who are experts in a domain.

Regarding concerns for budget of knowledge transfer, the management can have a document prepared by experts in a domain and this document can be uploaded on intranet. This can be made available to new joinees. Hence, additional human resource is not required for knowledge transfer and this will reduce the figures in the company's budget.

Now, if we observe carefully, the third solution may not be feasible all the time. We cannot be assured of expert professionals coming for interviews all the time. Moreover, expert professionals demand more compensation than not-so-expert professionals. This will increase the company's budget.

Hence, we will now combine the other three solutions to form a coherent one. The final solution will be for the management to first have a talk with the employees leaving the organization to know the reasons behind attrition, then come up with awards in suitable categories and then, create an easily and universally accessible document in the organization for knowledge transfer.

This way, analysis and synthesis together help in design thinking process. Design thinkers start with breaking down a problem into smaller problems that can be handled and studied easily. Then, the different solutions are combined to form a coherent single solution.

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*Written for Interactions magazine by Hugh Dubberly, Shelley Evenson, and Rick Robinson.*

The simplest way to describe the design process is to divide it into two phases: analysis and synthesis. Or preparation and inspiration. But those descriptions miss a crucial element—the connection between the two, the active move from one state to another, the transition or transformation that is at the heart of designing. How do designers move from analysis to synthesis? From problem to solution? From current situation to preferred future? From research to concept? From constituent needs to proposed response? From context to form?

How do designers bridge the gap?

The bridge model illustrates one way of thinking about the path from analysis to synthesis—the way in which the use of models to frame research results acts as a basis for framing possible futures. It says something more than “then the other thing happens.” It shows how designers and researchers move up through a level of analysis in order to move forward through time to the next desired state. And models act as the vehicle for that move.

The bridge model here is organized as a two-by-two matrix. The left column represents analysis (the problem, current situation, research, constituent needs, context). The right column represents synthesis (the solution, preferred future, concept, proposed response, form). The bottom row represents the concrete world we inhabit or could inhabit. The top row represents abstractions, models of what is or what could be, which we imagine and share with others.

**Analysis-Synthesis Bridge Model**

Ideally, the design process begins in the lower-left quadrant with observation and investigation—an inventory (or description) of the current situation. As the process moves forward, it moves to the upper-left quadrant. We make sense of research by analysis, filtering data we collect to highlight points we decide are important or using tools we’re comfortable with to sort, prioritize, and order. We frame the current situation, but move out of the strictly concrete. We define the problem. We interpret. Analysis begins as thoughtful reflection on the present and continues as conversation with the possible. Crucial for progress is documenting and visualizing our analysis, making it possible for us to come back to it, making it possible to imagine alternatives, making it possible ultimately to discuss and agree with others on our framing and definition. We might write down a list of findings or a statement defining the problem. Better still is writing a story. A story describes actors and actions; it suggests relationships, which we may represent in visual form. A story of what happens suggests a model of what is—an interpretation of our research. The process of coming to a shared representation externalizes individual thinking and helps build trust across disciplines and stakeholders.

Having agreed on a model of what is (framed the current situation, defined the problem) then the other side of the coin (the preferred future, the solution) is implied. An interpretation provides “a description of the everyday in such a way as to see how it might be different, better, or new [1].” We can devise stories about what could happen. We can model alternatives in relation to our first model. In doing so, we’ve moved to the upper-right quadrant, to the use and development of models of what could be. It is in the realm of abstraction—by thinking with models—that we bridge the gap between analysis and synthesis. These models are hypotheses, speculations, imagined alternatives to the concrete we started with, but they are still abstract themselves. It is easy to “play” with models at this point, to test and explore. But design requires that the work return to the concrete, that we make things real, realize our models as prototypes or even finished form. This is the lower-right quadrant. Of course, results improve with iteration. Submitting the new prototype to testing, further observation and investigation, continuing around the quadrants, we learn and refine our work. The bridge model has several antecedents and variations.

**Robinson Model**

The bridge model grew out of personal discussions over the past few years. Rick Robinson has written about “the space in between” research and concept. He has described anthropologist Clifford Geertz’s essay, “Deep Play: Notes on the Balinese Cockfight,” as an example of abstracting a model from research, and one that parallels strongly the moves that other forms of research and design make in moving from description through interpretation to application. “[The construct of] Deep Play becomes a lens through which Geertz can show what’s important about the Balinese cockfight, and his colleagues can understand important underlying factors in something like fan riots at soccer matches [1].”

**Beer Model**

Writing about the relationship of science to management, Stafford Beer presented a more elaborate model of the move from cases to consensus, from particular to general. He points out that several levels of models are involved [2].

**Alexander Model**

At the beginning of his career, Christopher Alexander described a six-part model. It differs from the bridge model in two important respects. First, Alexander explicitly separates the mental picture (model) from a formal picture of the mental picture (a representation of the model). Second, his notion of a model (at that time at least) was highly mathematical [3].

**Kumar Model**

Vijay Kumar has proposed a model of the innovation process.[4] He frames it as a two-by- two matrix, moving from research, to “Framing Insights,” “Exploring Concepts,” and “Making Plans.” He notes, “’Framing Insights’ are primarily about descriptive modeling, creating abstract mental pictures about the patterns that we recognize about reality. ‘Exploring Concepts’ and ‘Making Plans’ are about prescriptive modeling.” Where the bridge model forefronts the role of models, Kumar’s model forefronts steps that make use of modeling. He recently published a wonderful poster that maps the steps in the “innovation process” to a series of methods.

**Kaiser-IDEO Model**

During the process of writing this article, interactions co-editor Richard Anderson pointed out this model of the innovation process. Christi Zuber reports that Kaiser Permanente’s Innovation Center (working with IDEO) developed this model in 2004 as part of an innovation toolkit created for use inside Kaiser. This model is similar to Kumar’s model, but the Kaiser model emphasizes storytelling and brainstorming as key methods.

**Suri-IDEO Model**

Responding to questions about the origin of the Kaiser/IDEO model, Jane Fulton Suri supplied this recent model of the process of moving from synthesis to strategy. It shares the same basic structure as the Robinson model; though synthesis (depicted as the right column in other models) is here depicted as the left column. The framing of models as a link between patterns and principles is a useful addition [5].

While practitioners and educators increasingly make use of models, few forefront the role of modeling in public summaries of their work processes. Glossing over modeling can limit design to the world of form-making and misses an opportunity to push toward interaction and experience. We see modeling becoming an integral part of practice, especially in designing software, services, and other complex systems.

The bridge model makes explicit the role of modeling in the design process. Explicit modeling is useful in at least two ways. First, it accelerates the design process by encouraging team members to understand and agree on the elements of a system and how those elements interact with each other and their environment. Second, by making the elements and their interactions visible, it reduces the likelihood of overlooking differences in point of view, which might otherwise eventually derail a project.

Explicit modeling also helps scale the design process. It enables designers to develop larger and more complex systems and makes the process of working with larger and more complex organizations easier. Discussing the role of modeling in design also invites comparison and interaction with other disciplines that use models. Ideally, practitioners that use models may, over time, be able to see patterns across their models that will advance the practice of design.

Joanne Mendel and Jan Yeager build on the bridge model in their article [Knowledge Visualization in Design Practice][1].

[1]: http://piim.newschool.edu/journal/issues/2010/03/pdfs/ParsonsJournalForInformationMapping_Mendel-Joanne+Yeager-Jan.pdf “Knowledge Visualization in Design Practice”

Yggdrasil 2008 - Day 2 | Searchnuggets

Oct 19, 2008 5:53 am

[…] from Objectware gave us a seemingly complete walk through his UX toolbox. Referring to the analysis-synthesis bridge model, he described a large set of methods for user observation, interviews, workshop techniques, user […]

Yggdrasil 2008 - Day 2 - Things On Top

Jan 17, 2009 3:15 pm

Putting people first » Dubberly Design articles

Feb 13, 2009 3:09 am

[…] The analysis-synthesis bridge model Written for Interactions magazine by Hugh Dubberly, Shelley Evenson, and Rick Robinson – 1 March 2008 The simplest way to describe the design process is to divide it into two phases: analysis and synthesis. Or preparation and inspiration. But those descriptions miss a crucial element—the connection between the two, the active move from one state to another, the transition or transformation that is at the heart of designing. How do designers move from analysis to synthesis? From problem to solution? From current situation to preferred future? From research to concept? From constituent needs to proposed response? From context to form? […]

Jan 4, 2015 1:01 pm

Consider the primitive origins of analysis and synthesis in ancient or pre-historic times.

For example, a warrior finds a spear lost by another tribe. How will he make one like it? He dismantles in into separate pieces. (Analysis!) He spreads them out in an orderly pattern on a flat sheet or surface and, of course, exercises his imagination. (Sounds like “spreadsheeting” to me.) Then he puts the spear (or one like it) back together again. (Synthesis!)

The use of models (objects, pictures, symbols) representing other things seems to me to be the next step.

Thank you for an interesting scholarly article in this subject area. By the way, if there was an ancient word for “spreadsheeting”, I would be happy to learn of it.

Dec 28, 2021 10:41 pm

Could this not be deemed similar to the TRIZ analysis / synthesis model proposed by Altshuller?

https://upload.wikimedia.org/wikipedia/commons/a/a2/Prism_of_TRIZ_Oxford_Creativity.png

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Abductive Thinking, Conceptualization, and Design Synthesis

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• Dingzhou Fei 17  

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 876))

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Design synthesis is a process of cognitive development that aims to manage complexity or seek to avoid confusion. Design is always a comprehensive synthesis of market demand, technology trends and business needs. In the synthesis process, designers attempt to organize, manipulate, trim, and filter the collected data to form a cohesive conceptual construction system. The design synthesis reveals cohesion and continuity; the combination shows the improvement of the organization, the reduction of complexity and the formation of idealized clarity and conceptualization. However, this cognitive synthesis is often not so obvious or even completely hidden. This article attempts to define this reasoning process from the perspective of psychology and takes it seriously in its universal significance in the entire design process. This paper investigates that the following claims: (1) There are three types of applicability of abductive reasoning for design synthesis including: identification of implicit design targets, idealization of innovative design concepts, and diagnosis of violating design constraints or design axioms. These three components have a common basis: conceptualization and reconceptualization. They can be taken as sense making from chaos and uncertainty. (2) Synthesis is an abductive thinking process. Abductive reasoning related to insight and creative problem solving, and it is this creative problem solving that is at the heart of the design synthesis methods. (3) Conceptualization relates three specific sub-processes: prioritizing, judging, and forging. Conceptualization is changeable.

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Fei, D. (2019). Abductive Thinking, Conceptualization, and Design Synthesis. In: Ahram, T., Karwowski, W., Taiar, R. (eds) Human Systems Engineering and Design. IHSED 2018. Advances in Intelligent Systems and Computing, vol 876. Springer, Cham. https://doi.org/10.1007/978-3-030-02053-8_16

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Analysing UX research and synthesising results into valuable insights

Analysing UX research and synthesising the data that comes from it can play a huge part in the success of your end product. In this piece, we take a look at how analysing UX research will turn your findings into useful insights.

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UX research can be a valuable part of the UX design process. However, completing your study is only the first step in obtaining the insights you seek. Until you analyse the data you’ve collected into a set of findings, synthesise those findings into insights and interpret the results, you won’t be able to communicate how your research can help improve the user experience to your stakeholders and clients.

In this post, we’ll discuss how analysing UX research using quantitative and qualitative data will turn your findings into useful insights. We’ll also look at how to interpret your findings. Here’s what we’ll cover:

What is user research analysis?

When should you conduct user research analysis, what are the different types of user research analysis (quantitative vs. qualitative), how to analyse quantitative research data, how to analyse qualitative research data, what next synthesising and interpreting your user research data and presenting your findings.

User research analysis involves analysing the data you’ve collected during your study. Analysis can be done in a variety of ways. The kind of analysis you choose to perform will depend on whether you’ve collected qualitative or quantitative data and what you were hoping to learn from your study. However, no matter what method you use, the goal of analysis is to identify the factual results of the study. In other words, it’s during analysis that you use the data you collected to arrive at a set of research findings .

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What’s the difference between analysing UX research and synthesising UX research?

The findings you obtain from your analysis may be interesting, but they don’t provide any real insight until you engage in synthesis. Synthesis is the process of bringing all the findings from analysis together to extract insights and conclusions from the data, as well as a set of actionable recommendations for the UX design of the product. While analysis provides a set of facts, synthesis makes those facts meaningful.

Analysis and synthesis often happen at the same time . Yet, while we plan our analysis in anticipation of the questions we want to answer, synthesis is an emergent process through which we make connections and come up with possible insights as we go. Throughout this post, we’ll talk about analysis and synthesis separately, but in reality, these processes are likely to overlap.

The obvious answer to this question is after you’ve finished collecting data from your user research study, but you’ll need to think about analysis even before you start your user research. Before you begin, define a set of objectives and research questions that you want to answer and come up with ways to answer them.

Then, during the study, conduct analysis while your data is being collected. Analysis can help you ensure you’re asking the right questions. For example, if you’re conducting user interviews and you conduct periodic analysis, you may find out you’re asking the wrong questions—ones that don’t really pertain to the variable you want to explore. The good thing is, if this is the case and you’ve caught it early on, you haven’t wasted the whole study on the wrong variable.

In addition to finding any errors, starting analysis during your study is also more efficient. But the truth is the majority of the real work in user research analysis happens at the end of the study. No matter what, though, the important thing is that you finish analysing all the data you collected before you interpret the results and draw conclusions.

There are two types of user research, quantitative and qualitative , and they each require a different type of analysis. 

Quantitative research gathers objective, numerical data and can help you answer questions about things like success rates, task times, and error rates. Quantitative data tells you the “what” and the “how”—what do your users do / how do they interact with the product? 

Qualitative research gathers subjective, qualitative data, usually in the form of words (i.e. what the user says about their experience, how they feel, what they think about a product, etc.). When analysing qualitative research data, you’re looking for themes and patterns across users’ responses. Qualitative research can provide insight into what features are most important to users, how a given experience makes them feel, what they find difficult about a certain experience, and so on. Given the in-depth insights qualitative research can yield, analysing the results often takes longer than for quantitative research. 

Now let’s take a closer look at how to analyse both quantitative and qualitative data. 

If you’ve conducted a quantitative study, such as a survey with yes/no or multiple choice questions, an A/B test or an eye tracking test, you will be left with a large set of numerical data. Depending on how the data was collected, it will either already be laid out in a spreadsheet or will have to be entered into a spreadsheet manually, where each column corresponds to one question and each row includes one participant’s answers.

The dataset in the spreadsheet will then be analysed statistically. Programmes like R or SPSS can be used to run statistical analysis or formulas can be plugged into a Google or Excel spreadsheet.

Before you start running statistical formulas on your data, go back to the original goals of the study and decide exactly what questions you want to answer.

For example, maybe you’re curious to learn how long it takes a user to sign up for a newsletter. Or perhaps you’re trying to find out if users are satisfied with the various steps of a checkout process. No matter what the goals are, make sure to concentrate your analysis there.

The good thing about quantitative analysis is that once you decide on the variables you want to analyse, it can be extremely quick and efficient to perform. Quantitative findings, such as the average time to complete a task, participants’ satisfaction ratings with parts of a product or information on features they use the most, can lead to insights about whether the UX for a certain task should be refined and what features should be redesigned or eliminated from a product entirely. 

Quantitative data can also be analysed to compare and contrast the way users from different demographic groups use a product. These findings can provide insight into the different use cases UX designers must keep in mind as they’re creating the product’s user experience.

Learn more about quantitative analysis: The 7 Most Important UX KPIs (and how to measure them) .

If you’ve conducted a qualitative study, such as user interviews, focus groups or ethnographic research, you’ll be left with a large amount of information in the form of words. If your participants didn’t provide their answers in written form, you’ll want to have all of the interviews or responses transcribed so you can easily read what participants said. While it can be expensive, it’s worthwhile to use a service like Rev.com to transcribe your interviews so your time is freed up to focus on other tasks.

Once the data is transcribed, you can organise it in a number of ways. One is to put it in a spreadsheet where each row represents the answers provided by a single participant. Another option is to upload that data to a qualitative analysis tool like NVivo or Dedoose .

Just like with quantitative data, before you settle on a method for analysing the data qualitatively, you should revisit the original goals of your research and make sure that your analysis focuses on them.

For example, if you’re designing a real estate app where users can find houses for sale, you’ll want to focus on the demographics of potential users, what features they focus on most when searching for a home and what draws their attention to a given listing.

Although participants might have brought up other topics during your study, don’t include them in your analysis if they don’t pertain to your research goals.

There are several ways you can analyse qualitative data. Two popular options are content analysis and affinity mapping.

Content analysis

Content analysis involves looking for patterns in the data and then coding them. It can be especially useful for evaluating long text data such as interview transcripts. Codes are essentially labels that you can apply to each chunk of text that brings up a particular topic.

For example, for the real estate app mentioned above, you might use codes such as budget, location and number of bedrooms. As you go through the text data, you will then label each chunk of data where these subjects are discussed.

Here are the steps to perform a content analysis:

• Decide on codes. There are two ways to do this. On the one hand, you can come up with codes based on the topics you’re hoping to find. If you’re interested in seeing what people have to say on a house with a pool, you’ll include the code “pool” in your analysis. On the other hand, you can see what topics emerge organically. If you review the data and notice that decks come up frequently, you’ll include the code “deck” in your analysis.
• Assign codes as you work . This can be done manually or with a qualitative analysis program like those mentioned above.
• Organise related codes into themes . Once you’ve coded all the data, look for codes that speak to the same general topic and place these under larger umbrella categories called themes. For example, codes like “gated community,” “good schools” and “safe location” can be grouped into a theme called “neighbourhood.”

If more than one member of your team is coding the data, you need to make sure everyone understands the codes the same way. To do this, before coding the entire dataset, each coder should code the same small part of the data and compare their work. If there is disagreement in the way the codes are applied, coders need to discuss the discrepancies until they’ve agreed on how to apply the codes consistently.

Affinity mapping

Another useful way to analyse the data is through affinity mapping . Affinity mapping is a visual way of organising the data but, like content analysis, the overall goal is to identify patterns and themes.

Although this is not the only way to conduct affinity mapping, User Interviews recommends taking these four steps:

• Write all the text data points on post-it notes. While this will likely lead to a lot of post-its, make sure to use as many as your project requires.
• Stick all the post-its to a wall, whiteboard, or any other large surface that people can gather around.
• Move the post-its into groups based on common themes that pertain to your research objectives. You may want to limit this process to about 20-30 minutes depending on how many post-its you have to organise and to prevent the affinity mapping session from sprawling.
• Continue to re-organise the groups until time runs out or the team has come to a consensus. If time runs out and team members still disagree on the groups, have a discussion until everyone’s on the same page.

Once you complete this process, you may want to label each group with an overarching theme that sums up the content.

The final product of any user research analysis is interpreting your research and presenting your findings. Your presentation to stakeholders and clients should have sections for “key learnings” and “recommendations.” In the “key learnings” section you’ll interpret your research data so it provides value. For example, say one of your insights is that there was a theme of “budget” for the real estate app mentioned above. You interpret it in the form of a key learning, “Make sure to feature budget prominently and not share anything above this price range.” 

Then the “recommendation” makes this insight valuable by framing it as a recommended action. For example, the “recommendations” for the above key learning could be “Make budget a major section of the website” and “do not share anything above the user’s price range, if they’ve shared their budget.” When possible, it helps to combine qualitative and quantitative findings for the biggest impact.

Interpreting the data is where you’ll really probe the analysis for meaningful insights, and it’s these insights that ultimately have the greatest benefit of any user research. Interpretation enables us to truly understand what users want, and don’t want, from the product we’re designing. Interpreting the research data and delivering a presentation that shares these insights increases our chances of creating product users will love. You can learn more about how to present your UX research findings in this guide .

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3 September 2024

Systems Engineering

The four (4) steps that comprise the SE Process are:

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Green synthesis of MgO nanoparticles using the flower extracts of Madhuca longifolia and study of their morphological and antimicrobial properties

• Kurhade, Pranali
• Kodape, Shyam
• Junghare, Kunjan
• Wankhade, Atul

Wet chemical synthesis and a green synthesis approach have been used to produce magnesium oxide (MgO) nanoparticles. A floral extract of Madhuca longifolia (M. longifolia/mahua) was used as a reducing agent for magnesium nitrate hexahydrate and magnesium acetate tetrahydrate. Three different concentrations of floral extracts were used, along with two different concentrations of metal precursors. This study was designed to observe the influence of varying parameters on the particle size and shape of MgO nanoparticles. A Taguchi robust design approach was used to identify the factors that contribute most to the particle size and distribution of magnesium oxide nanoparticles, as well as the conditions that have the greatest impact. According to Taguchi analysis, the concentration of the metal precursor had the greatest influence on the size of the MgO nanoparticles. UV–visible spectroscopy, Fourier transform infrared spectroscopy (FTIR), field emission scanning electron microscopy (FESEM), energy-dispersive X-ray spectroscopy (EDS), and X-ray powder diffraction (XRD) analyses were conducted to demonstrate the effective synthesis of MgO nanoparticles. The findings showed a variety of nanoparticle morphologies and a cubic crystal structure with high purity MgO nanoparticle content. Additionally, the FTIR analysis reveals that floral extracts were actively involved in the synthesis process. It was determined that the average size of all 12 samples ranged between 30 and 100 nm. To investigate the antimicrobial activity of synthesized MgO nanoparticles, Escherichia coli and Staphylococcus aureus were used. The majority of the samples were found to be appropriately inhibitory against both microbial strains; average zones of inhibition were also noted, along with the determination of the best sample's minimum inhibitory concentration for both microorganisms. This is the first attempt to explore the effects of different factors on the structural morphology of MgO nanoparticles using mahua flower extracts.

• Magnesium oxide nanoparticles;
• Madhuca longifolia;
• Morphological study;
• Phytochemicals;
• Antimicrobial activity;
• Green synthesis

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JSmol Viewer

Core–shell interface engineering strategies for modulating energy transfer in rare earth-doped nanoparticles.

1. Introduction

2. materials and methods, 2.1. materials, 2.2. composition of renps, 2.3. method 1: one-pot successive lbl strategy, 2.3.1. syntheses of ce-renps shell precursors, 2.3.2. synthesis of ce-renps core, 2.3.3. synthesis of ce-renps-lbl core@shell 1 with different thicknesses of shell 1, 2.3.4. synthesis of ce-renps-lbl core@shell 1@shell 2, 2.4. method 2: modified sa growth strategy, 2.4.1. synthesis of ce-renps core, 2.4.2. syntheses of ce-renps shell 1 precursor and shell 2 precursor, 2.4.3. synthesis of ce-renps-sa core@shell 1 with different thicknesses of shell 1, 2.4.4. synthesis of ce-renps-sa core@shell 1@shell 2, 2.5. synthesis of ce-renps@sio 2 -nh 2, 2.6. synthesis of ce-renps@sio 2 -rb/fa, 2.7. cell culture, 2.8. ros detection, 2.9. in vivo imaging, 2.10. characterization, 3. results and discussion, 3.1. synthesis strategies and the interface characteristics, 3.2. interface clarity and energy transfer, 3.3. the orthogonality of luminescence and the thickness of the isolation layer, 3.4. ros generation and detection of ce-renps@sio 2 -rb in vitro and in vivo, 3.5. enhanced nir-ii imaging for sa strategy, 4. conclusions, supplementary materials, author contributions, data availability statement, conflicts of interest, abbreviations.

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Share and Cite

Zhou, Z.; Liu, Y.; Guo, L.; Wang, T.; Yan, X.; Wei, S.; Qiu, D.; Chen, D.; Zhang, X.; Ju, H. Core–Shell Interface Engineering Strategies for Modulating Energy Transfer in Rare Earth-Doped Nanoparticles. Nanomaterials 2024 , 14 , 1326. https://doi.org/10.3390/nano14161326

Zhou Z, Liu Y, Guo L, Wang T, Yan X, Wei S, Qiu D, Chen D, Zhang X, Ju H. Core–Shell Interface Engineering Strategies for Modulating Energy Transfer in Rare Earth-Doped Nanoparticles. Nanomaterials . 2024; 14(16):1326. https://doi.org/10.3390/nano14161326

Zhou, Zhaoxi, Yuan Liu, Lichao Guo, Tian Wang, Xinrong Yan, Shijiong Wei, Dehui Qiu, Desheng Chen, Xiaobo Zhang, and Huangxian Ju. 2024. "Core–Shell Interface Engineering Strategies for Modulating Energy Transfer in Rare Earth-Doped Nanoparticles" Nanomaterials 14, no. 16: 1326. https://doi.org/10.3390/nano14161326

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