Finally, I gave it to my tutor, who said that the depth of the paper was too shallow, but there was not enough time to experiment again. What should I do?
At this time, I can only work harder on my thesis writing. In fact, the paper review is not about how innovative it is, but about the depth of thinking, at least to meet the requirements of the degree.
How to improve the depth of the paper?
First, build a "forced" structural framework.
This step is very important for the height of the paper. For example, everyone will understand. Take dressing as the topic.
Qualified graduation thesis:
Chapter one, what is clothes?
Chapter two, why do people wear clothes?
The third chapter, the skills of clothing collocation.
Chapter four, new skills of clothing collocation.
Good graduation thesis:
Chapter one, the definition of dress and its historical development.
Chapter two, the comparison of different dressing skills.
The third chapter, the influence of height, gender and personality on dress collocation.
The fourth chapter, how to show the effect of clothing collocation scientifically.
Chapter five, the summary of the best way to dress.
Excellent graduation thesis:
The first chapter, the origin of clothing collocation, its value to human society and its design deficiencies.
The second chapter, based on the increase of sensitivity of biomolecules secreted by dopamine and androgen to clothing collocation and its characterization.
In the third chapter, based on Navier-Stokes/Fourier/Fick equation, the visual sensitivity and heat transfer model in the process of clothing collocation are established.
The fourth chapter, the concept, principles, preparation methods and application scenarios of high-end clothing collocation.
In a word, scientific research should be microscopic, and the implementation of the paper means that the content of the framework should be microscopic.
Second, increase data performance analysis, especially the expression techniques on the tall.
You can refer to excellent doctoral dissertations. Analysis: Let's see what characterization analysis predecessors have made on the same experiment. For data analysis that you haven't done, contact the experimental center for testing and add it to your paper.
Pay attention, don't be afraid to spend money on experiments, and use advanced instruments in high buildings to characterize them.
For example, the topic "Study on the Properties of Sulfur Doped Graphene". Advanced characterization means such as scanning electron microscope (SEM), atomic force microscope (AFM) and X-ray diffraction (XRD).
Third, increase the depth of characterization data analysis.
Such an expensive representation means to make high-quality representation pictures will improve the grade of the paper as a whole.
It is also a pity that a high-quality map is not set off by profound analysis.
For example, the XRD chart is very good, but what if I can't analyze it? This is a long-term process of learning and accumulating points. If you don't have time, you have to have the cheek to ask Shuobo's brothers and sisters. They work with teachers all the year round and have rich experience.
If you really can't pull your face down, there is another way, and that is to consult the literature. For the same element, the characteristics reflected in the spectrum are consistent. So reference is also a simple and quick method.
Fourth, the language of the paper.
It is also easy to find the trick, that is, the language should avoid vernacular, common words and common words, and try to use obscure words.
Give a few examples. The expression "I did two experiments" should say "a series of experimental data show".
When you say "brother and sister say", you say "as we all know". The expression "the result is wrong" should say "in theory, we can draw such a conclusion. However, the experimental results obtained are just the opposite. The error may come from the following aspects. " .
To sum up, "a man can do it, and he has done it;" Ten people can do it, and there are already thousands. "The rapid promotion of the paper is based on a large number of references.